From 0ce32a4f6f4b146b27e8e2a347afd032177a0b97 Mon Sep 17 00:00:00 2001 From: Julien Look <53651363+juice1000@users.noreply.github.com> Date: Mon, 23 Mar 2020 21:35:34 +0100 Subject: [PATCH 01/63] CARTPOLE - create vanilla a2c and synchronous a2c --- a2c_cart.py | 151 +++++++++++++++++++++++++++++++++++++++++++++++ sa2c_cart.py | 162 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 313 insertions(+) create mode 100644 a2c_cart.py create mode 100644 sa2c_cart.py diff --git a/a2c_cart.py b/a2c_cart.py new file mode 100644 index 0000000..636e2fa --- /dev/null +++ b/a2c_cart.py @@ -0,0 +1,151 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import gym +import os + +import argparse +import time +from datetime import datetime +timestr = time.strftime("%d.%m.%Y - %H:%M:%S") + +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 500 +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0') +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") + parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + global args + args = parser.parse_args() + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 128) + self.pi2 = nn.Linear(128, a_dim) + self.v1 = nn.Linear(s_dim, 128) + self.v2 = nn.Linear(128, 1) + set_init([self.pi1, self.pi2, self.v1, self.v2]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = torch.tanh(self.pi1(x)) + logits = self.pi2(pi1) + v1 = torch.tanh(self.v1(x)) + values = self.v2(v1) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.normal_(layer.weight, mean=0., std=0.1) + nn.init.constant_(layer.bias, 0.) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + + def run(self): + global episode + total_step = 1 + while self.g_ep.value < MAX_EP: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + if self.name == 'w00': + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: # done and print information + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + break + s = s_ + total_step += 1 + self.res_queue.put(None) + + +if __name__ == "__main__": + + gnet = Net(N_S, N_A) # global network + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() + + # parallel training + worker = Worker(gnet, opt, global_ep, global_ep_r, res_queue, 0) + worker.start() + res = [] # record episode reward to plot + while True: + r = res_queue.get() + if r is not None: + res.append(r) + else: + break + + + import matplotlib.pyplot as plt + + plt.plot(res) + plt.ylabel('Average Reward') + plt.xlabel('Episode') + plt.show() + diff --git a/sa2c_cart.py b/sa2c_cart.py new file mode 100644 index 0000000..d259ecf --- /dev/null +++ b/sa2c_cart.py @@ -0,0 +1,162 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. +View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/). +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import gym +import os + +import argparse +import time +from datetime import datetime +timestr = time.strftime("%d.%m.%Y - %H:%M:%S") + +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 500 +GAMMA = 0.9 +MAX_EP = 3000 +episode = 0 + +env = gym.make('CartPole-v0') +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") + parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + global args + args = parser.parse_args() + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 128) + self.pi2 = nn.Linear(128, a_dim) + self.v1 = nn.Linear(s_dim, 128) + self.v2 = nn.Linear(128, 1) + set_init([self.pi1, self.pi2, self.v1, self.v2]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = torch.tanh(self.pi1(x)) + logits = self.pi2(pi1) + v1 = torch.tanh(self.v1(x)) + values = self.v2(v1) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.normal_(layer.weight, mean=0., std=0.1) + nn.init.constant_(layer.bias, 0.) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + + def run(self): + global episode + total_step = 1 + while self.g_ep.value < MAX_EP: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + if self.name == 'w00': + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net + # sync + if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 and self.g_ep.value != 0: + print (self.g_ep.value) + print("sleep...") + self.res_queue.put(time.sleep(10)) + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: # done and print information + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + break + s = s_ + total_step += 1 + self.res_queue.put(None) + + +if __name__ == "__main__": + + gnet = Net(N_S, N_A) # global network + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() + + # parallel training + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + res = [] # record episode reward to plot + while True: + #print ("Episode: ", global_ep.value) + r = res_queue.get() + if r is not None: + res.append(r) + #if global_ep.value % 500 == 0: + #res.put(time.sleep(5)) + #[w.join() for w in workers] + elif global_ep.value == MAX_EP: + break + else: + True + + import matplotlib.pyplot as plt + + plt.plot(res) + plt.ylabel('Average Reward') + plt.xlabel('Episode') + plt.show() + From 4918b37641c4b7ff9d3dd9f37a7a9b105e7564c5 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 24 Mar 2020 08:49:08 +0100 Subject: [PATCH 02/63] CARTPOLE - test --- discrete_A3C.py | 2 ++ results/pendulum.png | Bin 77730 -> 0 bytes 2 files changed, 2 insertions(+) delete mode 100644 results/pendulum.png diff --git a/discrete_A3C.py 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wAjU&gi%$3F`_ItkNZ0?j;mQ9=#M5Wb-|)fux05FJLk2^7zudlvJ*ThzHwaQ@FaQ7m From 63b409269684b0d8268532dfceec17028ef68555 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 24 Mar 2020 23:16:33 +0100 Subject: [PATCH 03/63] CARTPOLE - renew model and parameters, add args and plotter function, terminate after score of 600, save and load model --- a2c_cart.py | 95 ++++++++++++++++++---------- sa2c_cart.py => a2c_sync_cart.py | 104 ++++++++++++++++++------------- discrete_A3C.py => a3c_cart.py | 89 +++++++++++++++++++------- shared_adam.py | 2 +- 4 files changed, 192 insertions(+), 98 deletions(-) rename sa2c_cart.py => a2c_sync_cart.py (66%) rename discrete_A3C.py => a3c_cart.py (61%) diff --git a/a2c_cart.py b/a2c_cart.py index 636e2fa..c027fbc 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -1,6 +1,7 @@ """ Reinforcement Learning (A3C) using Pytroch + multiprocessing. The most simple implementation for continuous action. + """ import torch @@ -10,24 +11,32 @@ import torch.multiprocessing as mp from shared_adam import SharedAdam import gym +import sys import os +os.environ["OMP_NUM_THREADS"] = "1" import argparse +import matplotlib.pyplot as plt import time -from datetime import datetime -timestr = time.strftime("%d.%m.%Y - %H:%M:%S") -os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 500 +UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 5000 + + env = gym.make('CartPole-v0') N_S = env.observation_space.shape[0] N_A = env.action_space.n +def plotter(): + plt.plot(res) + plt.ylabel('Average Reward') + plt.xlabel('Step') + plt.show() + + def handleArguments(): """Handles CLI arguments and saves them globally""" parser = argparse.ArgumentParser( @@ -43,24 +52,28 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 128) - self.pi2 = nn.Linear(128, a_dim) - self.v1 = nn.Linear(s_dim, 128) - self.v2 = nn.Linear(128, 1) - set_init([self.pi1, self.pi2, self.v1, self.v2]) + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): - pi1 = torch.tanh(self.pi1(x)) - logits = self.pi2(pi1) - v1 = torch.tanh(self.v1(x)) - values = self.v2(v1) + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) return logits, values def set_init(layers): for layer in layers: - nn.init.normal_(layer.weight, mean=0., std=0.1) - nn.init.constant_(layer.bias, 0.) + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) def choose_action(self, s): self.eval() @@ -89,18 +102,18 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network + self.lnet = Net(N_S, N_A) # local network self.env = gym.make('CartPole-v0').unwrapped def run(self): - global episode total_step = 1 - while self.g_ep.value < MAX_EP: + stop_processes = False + while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if self.name == 'w00': + if self.name == 'w00' and args.demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = self.env.step(a) @@ -112,22 +125,38 @@ def run(self): if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] + if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 and self.g_ep.value != 0: + print(self.g_ep.value) + print("sleep...") + self.res_queue.put(time.sleep(3)) + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) break + + if self.g_ep_r.value > 600: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True + break s = s_ total_step += 1 self.res_queue.put(None) if __name__ == "__main__": - - gnet = Net(N_S, N_A) # global network - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer + handleArguments() + # load global network + if args.load_model: + gnet = Net(N_S, N_A) + gnet.load_state_dict(torch.load("./save_model/a2c_cart.pt")) + gnet.eval() + else: + gnet = Net(N_S, N_A) + + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -136,16 +165,14 @@ def run(self): res = [] # record episode reward to plot while True: r = res_queue.get() + score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) else: break + if score > 600: + torch.save(gnet.state_dict(), "./save_model/a2c_cart.pt") - import matplotlib.pyplot as plt - - plt.plot(res) - plt.ylabel('Average Reward') - plt.xlabel('Episode') - plt.show() - + plotter() + sys.exit() diff --git a/sa2c_cart.py b/a2c_sync_cart.py similarity index 66% rename from sa2c_cart.py rename to a2c_sync_cart.py index d259ecf..7acee82 100644 --- a/sa2c_cart.py +++ b/a2c_sync_cart.py @@ -1,7 +1,7 @@ """ Reinforcement Learning (A3C) using Pytroch + multiprocessing. The most simple implementation for continuous action. -View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/). + """ import torch @@ -11,25 +11,32 @@ import torch.multiprocessing as mp from shared_adam import SharedAdam import gym +import sys import os +os.environ["OMP_NUM_THREADS"] = "1" import argparse +import matplotlib.pyplot as plt import time -from datetime import datetime -timestr = time.strftime("%d.%m.%Y - %H:%M:%S") -os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 500 +UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 3000 -episode = 0 +MAX_EP = 5000 + + env = gym.make('CartPole-v0') N_S = env.observation_space.shape[0] N_A = env.action_space.n +def plotter(): + plt.plot(res) + plt.ylabel('Average Reward') + plt.xlabel('Step') + plt.show() + + def handleArguments(): """Handles CLI arguments and saves them globally""" parser = argparse.ArgumentParser( @@ -45,24 +52,28 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 128) - self.pi2 = nn.Linear(128, a_dim) - self.v1 = nn.Linear(s_dim, 128) - self.v2 = nn.Linear(128, 1) - set_init([self.pi1, self.pi2, self.v1, self.v2]) + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): - pi1 = torch.tanh(self.pi1(x)) - logits = self.pi2(pi1) - v1 = torch.tanh(self.v1(x)) - values = self.v2(v1) + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) return logits, values def set_init(layers): for layer in layers: - nn.init.normal_(layer.weight, mean=0., std=0.1) - nn.init.constant_(layer.bias, 0.) + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) def choose_action(self, s): self.eval() @@ -91,18 +102,18 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network + self.lnet = Net(N_S, N_A) # local network self.env = gym.make('CartPole-v0').unwrapped def run(self): - global episode total_step = 1 - while self.g_ep.value < MAX_EP: + stop_processes = False + while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if self.name == 'w00': + if self.name == 'w00' and args.demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = self.env.step(a) @@ -115,48 +126,57 @@ def run(self): if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net # sync if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 and self.g_ep.value != 0: - print (self.g_ep.value) + print(self.g_ep.value) print("sleep...") - self.res_queue.put(time.sleep(10)) + self.res_queue.put(time.sleep(3)) push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) break + + if self.g_ep_r.value > 600: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True + break s = s_ total_step += 1 self.res_queue.put(None) if __name__ == "__main__": - - gnet = Net(N_S, N_A) # global network - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer + handleArguments() + # load global network + if args.load_model: + gnet = Net(N_S, N_A) + gnet.load_state_dict(torch.load("./save_model/a2c_sync_cart.pt")) + gnet.eval() + else: + gnet = Net(N_S, N_A) + + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] [w.start() for w in workers] - res = [] # record episode reward to plot + res = [] # record episode reward to plot while True: - #print ("Episode: ", global_ep.value) r = res_queue.get() + score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) - #if global_ep.value % 500 == 0: - #res.put(time.sleep(5)) - #[w.join() for w in workers] - elif global_ep.value == MAX_EP: - break else: - True + break - import matplotlib.pyplot as plt + if score > 600: + torch.save(gnet.state_dict(), "./save_model/a2c_sync_cart.pt") + + + [w.join() for w in workers] + plotter() + sys.exit() - plt.plot(res) - plt.ylabel('Average Reward') - plt.xlabel('Episode') - plt.show() diff --git a/discrete_A3C.py b/a3c_cart.py similarity index 61% rename from discrete_A3C.py rename to a3c_cart.py index a3fe136..5761ee9 100644 --- a/discrete_A3C.py +++ b/a3c_cart.py @@ -2,7 +2,6 @@ Reinforcement Learning (A3C) using Pytroch + multiprocessing. The most simple implementation for continuous action. -View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/). """ import torch @@ -12,12 +11,16 @@ import torch.multiprocessing as mp from shared_adam import SharedAdam import gym +import sys import os os.environ["OMP_NUM_THREADS"] = "1" -UPDATE_GLOBAL_ITER = 5 +import argparse +import matplotlib.pyplot as plt + +UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 5000 @@ -26,25 +29,51 @@ N_A = env.action_space.n +def plotter(): + plt.plot(res) + plt.ylabel('Average Reward') + plt.xlabel('Step') + plt.show() + + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") + parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + global args + args = parser.parse_args() + + class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 128) - self.pi2 = nn.Linear(128, a_dim) - self.v1 = nn.Linear(s_dim, 128) - self.v2 = nn.Linear(128, 1) - set_init([self.pi1, self.pi2, self.v1, self.v2]) + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): - pi1 = torch.tanh(self.pi1(x)) - logits = self.pi2(pi1) - v1 = torch.tanh(self.v1(x)) - values = self.v2(v1) + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) return logits, values + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + def choose_action(self, s): self.eval() logits, _ = self.forward(s) @@ -77,12 +106,13 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): def run(self): total_step = 1 - while self.g_ep.value < MAX_EP: + stop_processes = False + while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if self.name == 'w00': + if self.name == 'w00' and args.demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = self.env.step(a) @@ -100,15 +130,28 @@ def run(self): if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) break + + if self.g_ep_r.value > 600: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True + break s = s_ total_step += 1 self.res_queue.put(None) if __name__ == "__main__": - gnet = Net(N_S, N_A) # global network + handleArguments() + # load global network + if args.load_model: + gnet = Net(N_S, N_A) + gnet.load_state_dict(torch.load("./save_model/a3c_cart.pt")) + gnet.eval() + else: + gnet = Net(N_S, N_A) + gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -117,14 +160,18 @@ def run(self): res = [] # record episode reward to plot while True: r = res_queue.get() + score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) else: break + + if score > 600: + torch.save(gnet.state_dict(), "./save_model/a3c_cart.pt") + + [w.join() for w in workers] + plotter() + sys.exit() + - import matplotlib.pyplot as plt - plt.plot(res) - plt.ylabel('Moving average ep reward') - plt.xlabel('Step') - plt.show() diff --git a/shared_adam.py b/shared_adam.py index 5ddb28d..ec00f89 100644 --- a/shared_adam.py +++ b/shared_adam.py @@ -6,7 +6,7 @@ class SharedAdam(torch.optim.Adam): - def __init__(self, params, lr=1e-3, betas=(0.9, 0.99), eps=1e-8, + def __init__(self, params, lr, betas, eps=1e-8, weight_decay=0): super(SharedAdam, self).__init__(params, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) # State initialization From a459f8197f631064c7d4b20cd7a5c207ae16f1fc Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 25 Mar 2020 16:12:14 +0100 Subject: [PATCH 04/63] CARTPOLE - for all: terminate when score higher than 500 within range --- a2c_cart.py | 31 +++++++++++++++---------------- a2c_sync_cart.py | 20 +++++++++----------- a3c_cart.py | 19 ++++++++++--------- 3 files changed, 34 insertions(+), 36 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index c027fbc..6f85552 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -10,6 +10,7 @@ import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam +import numpy as np import gym import sys import os @@ -21,7 +22,7 @@ UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 5000 +MAX_EP = mp.cpu_count()*1000 @@ -33,7 +34,7 @@ def plotter(): plt.plot(res) plt.ylabel('Average Reward') - plt.xlabel('Step') + plt.xlabel('Episode') plt.show() @@ -108,6 +109,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): def run(self): total_step = 1 stop_processes = False + scores = [] while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] @@ -123,23 +125,22 @@ def run(self): buffer_s.append(s) buffer_r.append(r) + if self.g_ep_r.value >= 100: + self.g_ep_r.value = 100 + if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net # sync - if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 and self.g_ep.value != 0: - print(self.g_ep.value) - print("sleep...") - self.res_queue.put(time.sleep(3)) - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if np.mean(scores[-min(10, len(scores)):]) >= 500: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True break - if self.g_ep_r.value > 600: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - stop_processes = True - break s = s_ total_step += 1 self.res_queue.put(None) @@ -163,16 +164,14 @@ def run(self): worker = Worker(gnet, opt, global_ep, global_ep_r, res_queue, 0) worker.start() res = [] # record episode reward to plot + while True: r = res_queue.get() - score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) else: break - if score > 600: - torch.save(gnet.state_dict(), "./save_model/a2c_cart.pt") - + torch.save(gnet.state_dict(), "./save_model/a2c_cart.pt") plotter() sys.exit() diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 7acee82..476fd94 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -10,6 +10,7 @@ import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam +import numpy as np import gym import sys import os @@ -21,7 +22,7 @@ UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 5000 +MAX_EP = mp.cpu_count()*1000 @@ -33,7 +34,7 @@ def plotter(): plt.plot(res) plt.ylabel('Average Reward') - plt.xlabel('Step') + plt.xlabel('Episode') plt.show() @@ -108,6 +109,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): def run(self): total_step = 1 stop_processes = False + scores = [] while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] @@ -134,12 +136,12 @@ def run(self): if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if np.mean(scores[-min(mp.cpu_count() * 10, len(scores)):]) > 500: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True break - if self.g_ep_r.value > 600: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - stop_processes = True - break s = s_ total_step += 1 self.res_queue.put(None) @@ -165,17 +167,13 @@ def run(self): res = [] # record episode reward to plot while True: r = res_queue.get() - score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) else: break - if score > 600: - torch.save(gnet.state_dict(), "./save_model/a2c_sync_cart.pt") - - [w.join() for w in workers] + torch.save(gnet.state_dict(), "./save_model/a2c_sync_cart.pt") plotter() sys.exit() diff --git a/a3c_cart.py b/a3c_cart.py index 5761ee9..0e9c2f0 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -10,6 +10,7 @@ import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam +import numpy as np import gym import sys import os @@ -20,7 +21,7 @@ UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 5000 +MAX_EP = mp.cpu_count()*1000 @@ -32,7 +33,7 @@ def plotter(): plt.plot(res) plt.ylabel('Average Reward') - plt.xlabel('Step') + plt.xlabel('Episode') plt.show() @@ -107,6 +108,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): def run(self): total_step = 1 stop_processes = False + scores = [] while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] @@ -122,6 +124,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) + if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) @@ -129,12 +132,12 @@ def run(self): if done: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if np.mean(scores[-min(10, len(scores)):]) > 500: + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + stop_processes = True break - if self.g_ep_r.value > 600: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - stop_processes = True - break s = s_ total_step += 1 self.res_queue.put(None) @@ -166,11 +169,9 @@ def run(self): else: break - if score > 600: - torch.save(gnet.state_dict(), "./save_model/a3c_cart.pt") - [w.join() for w in workers] + torch.save(gnet.state_dict(), "./save_model/a3c_cart.pt") plotter() sys.exit() From 20458699df7e4c08fe48a2bad173c636804ec52e Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 25 Mar 2020 22:23:10 +0100 Subject: [PATCH 05/63] CARTPOLE - refactor a2c to without torch.mp module --- a2c_cart.py | 130 ++++++++++++++++++++++++++-------------------------- 1 file changed, 66 insertions(+), 64 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 6f85552..e27728e 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record +from utils import v_wrap, set_init import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam @@ -20,9 +20,8 @@ import matplotlib.pyplot as plt import time -UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = mp.cpu_count()*1000 +MAX_EP = 1000 @@ -48,6 +47,31 @@ def handleArguments(): args = parser.parse_args() +def push_and_pull(opt, gnet, done, s_, bs, ba, br, gamma): + if done: + v_s_ = 0. # terminal + else: + v_s_ = gnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + + buffer_v_target = [] + for r in br[::-1]: # reverse buffer r + v_s_ = r + gamma * v_s_ + buffer_v_target.append(v_s_) + buffer_v_target.reverse() + + loss = gnet.loss_func( + v_wrap(np.vstack(bs)), + v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), + v_wrap(np.array(buffer_v_target)[:, None])) + + # calculate local gradients and push local parameters to global + opt.zero_grad() + loss.backward() + opt.step() + + # pull global parameters + gnet.load_state_dict(gnet.state_dict()) + class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() @@ -97,55 +121,6 @@ def loss_func(self, s, a, v_t): return total_loss -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): - super(Worker, self).__init__() - self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - - def run(self): - total_step = 1 - stop_processes = False - scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - if self.name == 'w00' and args.demo_mode: - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if self.g_ep_r.value >= 100: - self.g_ep_r.value = 100 - - if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - if done: # done and print information - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(10, len(scores)):]) >= 500: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - stop_processes = True - break - - s = s_ - total_step += 1 - self.res_queue.put(None) - - if __name__ == "__main__": handleArguments() # load global network @@ -156,21 +131,48 @@ def run(self): else: gnet = Net(N_S, N_A) - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() + opt = SharedAdam(gnet.parameters(), lr=0.01, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r = 1, 0. - # parallel training - worker = Worker(gnet, opt, global_ep, global_ep_r, res_queue, 0) - worker.start() res = [] # record episode reward to plot - while True: - r = res_queue.get() - if r is not None: - res.append(r) - else: - break + name = 'w00' + total_step = 1 + stop_processes = False + scores = [] + while global_ep < MAX_EP and stop_processes is False: + s = env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + if name == 'w00' and args.demo_mode: + env.render() + a = gnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + if done or ep_r >= 500: # update network + # sync + push_and_pull(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + #record: global_ep, global_ep_r and ep_r + if global_ep_r == 0.: + global_ep_r = ep_r + else: + + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) + scores.append(int(global_ep_r)) + if np.mean(scores[-min(10, len(scores)):]) >= 500: + stop_processes = True + break + s = s_ + total_step += 1 torch.save(gnet.state_dict(), "./save_model/a2c_cart.pt") plotter() From 490767aaa372ab36543fa750cd8e40b165d4cb40 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 27 Mar 2020 22:20:28 +0100 Subject: [PATCH 06/63] CARTPOLE - stabilize algorithms: true synchronous learning for a2c_sync, set upper limit for ep_length, gym make unwrapped for a2c --- a2c_cart.py | 21 +++++++++++-------- a2c_sync_cart.py | 54 +++++++++++++++++++++++++++++++++++++----------- a3c_cart.py | 21 +++++++++++-------- 3 files changed, 66 insertions(+), 30 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index e27728e..b0da221 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -21,18 +21,18 @@ import time GAMMA = 0.9 -MAX_EP = 1000 +MAX_EP = 5000 -env = gym.make('CartPole-v0') +env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] N_A = env.action_space.n def plotter(): plt.plot(res) - plt.ylabel('Average Reward') + plt.ylabel('Score') plt.xlabel('Episode') plt.show() @@ -126,12 +126,12 @@ def loss_func(self, s, a, v_t): # load global network if args.load_model: gnet = Net(N_S, N_A) - gnet.load_state_dict(torch.load("./save_model/a2c_cart.pt")) + gnet = torch.load("./save_model/a3c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) - opt = SharedAdam(gnet.parameters(), lr=0.01, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r = 1, 0. res = [] # record episode reward to plot @@ -154,17 +154,16 @@ def loss_func(self, s, a, v_t): buffer_a.append(a) buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 500: # update network + if done or ep_r == 700: # update network # sync push_and_pull(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] global_ep += 1 - #record: global_ep, global_ep_r and ep_r + if global_ep_r == 0.: global_ep_r = ep_r else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) scores.append(int(global_ep_r)) @@ -174,6 +173,10 @@ def loss_func(self, s, a, v_t): s = s_ total_step += 1 - torch.save(gnet.state_dict(), "./save_model/a2c_cart.pt") + if np.mean(scores[-min(10, len(scores)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart.pt") + else: + print("Failed to train agent. Model was not saved") plotter() sys.exit() diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 476fd94..5b98650 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -48,6 +48,31 @@ def handleArguments(): args = parser.parse_args() +def optimize(opt, lnet, done, s_, bs, ba, br, gamma): + if done: + v_s_ = 0. # terminal + else: + v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + + buffer_v_target = [] + for r in br[::-1]: # reverse buffer r + v_s_ = r + gamma * v_s_ + buffer_v_target.append(v_s_) + buffer_v_target.reverse() + + loss = lnet.loss_func( + v_wrap(np.vstack(bs)), + v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), + v_wrap(np.array(buffer_v_target)[:, None])) + + # calculate local gradients + opt.zero_grad() + loss.backward() + opt.step() + + #lnet.load_state_dict(lnet.state_dict()) + + class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() @@ -125,20 +150,20 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net + if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 700: # update global and assign to local net # sync - if self.g_ep.value % UPDATE_GLOBAL_ITER == 0 and self.g_ep.value != 0: - print(self.g_ep.value) - print("sleep...") - self.res_queue.put(time.sleep(3)) + if total_step % UPDATE_GLOBAL_ITER == 0 and self.g_ep != 0: + time.sleep(1) push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] + else: + optimize(self.opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + + buffer_s, buffer_a, buffer_r = [], [], [] - if done: # done and print information + if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(mp.cpu_count() * 10, len(scores)):]) > 500: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: stop_processes = True break @@ -152,13 +177,13 @@ def run(self): # load global network if args.load_model: gnet = Net(N_S, N_A) - gnet.load_state_dict(torch.load("./save_model/a2c_sync_cart.pt")) + gnet = torch.load("./save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -173,7 +198,12 @@ def run(self): break [w.join() for w in workers] - torch.save(gnet.state_dict(), "./save_model/a2c_sync_cart.pt") + + if global_ep_r.value >= 300: + print("Save model") + torch.save(gnet, "./save_model/a2c_sync_cart.pt") + else: + print("Failed to train agent. Model was not saved") plotter() sys.exit() diff --git a/a3c_cart.py b/a3c_cart.py index 0e9c2f0..4736880 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -21,7 +21,7 @@ UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = mp.cpu_count()*1000 +MAX_EP = mp.cpu_count()*100 @@ -125,16 +125,15 @@ def run(self): buffer_r.append(r) - if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net + if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 700: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] - if done: # done and print information + if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(10, len(scores)):]) > 500: - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: stop_processes = True break @@ -148,13 +147,13 @@ def run(self): # load global network if args.load_model: gnet = Net(N_S, N_A) - gnet.load_state_dict(torch.load("./save_model/a3c_cart.pt")) + gnet = torch.load("./save_model/a3c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -163,7 +162,6 @@ def run(self): res = [] # record episode reward to plot while True: r = res_queue.get() - score = res_queue.get(global_ep_r.value) if r is not None: res.append(r) else: @@ -171,7 +169,12 @@ def run(self): [w.join() for w in workers] - torch.save(gnet.state_dict(), "./save_model/a3c_cart.pt") + + if global_ep_r.value >= 300: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart.pt") + else: + print("Failed to train agent. Model was not saved") plotter() sys.exit() From b7a4bf612f09278b85effd25d459172028c546ad Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sat, 28 Mar 2020 22:26:13 +0100 Subject: [PATCH 07/63] CARTPOLE - training duration in plotter, args and plotter in utils, save model if consecutive reward over 300 --- a2c_cart.py | 37 +++++++++++++------------------------ a2c_sync_cart.py | 47 ++++++++++++++++++----------------------------- a3c_cart.py | 46 +++++++++++++++++----------------------------- utils.py | 33 ++++++++++++++++++++++++++++----- 4 files changed, 76 insertions(+), 87 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index b0da221..41da625 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -6,18 +6,16 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init +from utils import v_wrap, set_init, plotter, handleArguments import torch.nn.functional as F -import torch.multiprocessing as mp from shared_adam import SharedAdam import numpy as np import gym +from datetime import datetime import sys import os os.environ["OMP_NUM_THREADS"] = "1" -import argparse -import matplotlib.pyplot as plt import time GAMMA = 0.9 @@ -30,22 +28,6 @@ N_A = env.action_space.n -def plotter(): - plt.plot(res) - plt.ylabel('Score') - plt.xlabel('Episode') - plt.show() - - -def handleArguments(): - """Handles CLI arguments and saves them globally""" - parser = argparse.ArgumentParser( - description="Switch between modes in A2C or loading models from previous games") - parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") - parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") - global args - args = parser.parse_args() - def push_and_pull(opt, gnet, done, s_, bs, ba, br, gamma): if done: @@ -122,9 +104,11 @@ def loss_func(self, s, a, v_t): if __name__ == "__main__": - handleArguments() # load global network - if args.load_model: + print ("Starting A2C Agent for Cartpole-v0") + time.sleep(3) + starttime = datetime.now() + if handleArguments().load_model: gnet = Net(N_S, N_A) gnet = torch.load("./save_model/a3c_cart.pt") gnet.eval() @@ -145,7 +129,7 @@ def loss_func(self, s, a, v_t): buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if name == 'w00' and args.demo_mode: + if name == 'w00' and handleArguments().demo_mode: env.render() a = gnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = env.step(a) @@ -178,5 +162,10 @@ def loss_func(self, s, a, v_t): torch.save(gnet, "./save_model/a3c_cart.pt") else: print("Failed to train agent. Model was not saved") - plotter() + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + plotter(scores, timedelta) + + sys.exit() diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 5b98650..1cada04 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -6,48 +6,31 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record +from utils import v_wrap, set_init, push_and_pull, record, plotter, handleArguments import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam import numpy as np import gym +import time +from datetime import datetime import sys import os os.environ["OMP_NUM_THREADS"] = "1" -import argparse -import matplotlib.pyplot as plt -import time + UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = mp.cpu_count()*1000 +MAX_EP = 5000 -env = gym.make('CartPole-v0') +env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] N_A = env.action_space.n -def plotter(): - plt.plot(res) - plt.ylabel('Average Reward') - plt.xlabel('Episode') - plt.show() - - -def handleArguments(): - """Handles CLI arguments and saves them globally""" - parser = argparse.ArgumentParser( - description="Switch between modes in A2C or loading models from previous games") - parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") - parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") - global args - args = parser.parse_args() - - def optimize(opt, lnet, done, s_, bs, ba, br, gamma): if done: v_s_ = 0. # terminal @@ -140,7 +123,7 @@ def run(self): buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if self.name == 'w00' and args.demo_mode: + if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = self.env.step(a) @@ -173,9 +156,11 @@ def run(self): if __name__ == "__main__": - handleArguments() # load global network - if args.load_model: + print("Starting Synchronous A2C Agent for Cartpole-v0") + time.sleep(3) + starttime = datetime.now() + if handleArguments().load_model: gnet = Net(N_S, N_A) gnet = torch.load("./save_model/a2c_sync_cart.pt") gnet.eval() @@ -183,7 +168,7 @@ def run(self): gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -199,12 +184,16 @@ def run(self): [w.join() for w in workers] - if global_ep_r.value >= 300: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: print("Save model") torch.save(gnet, "./save_model/a2c_sync_cart.pt") else: print("Failed to train agent. Model was not saved") - plotter() + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + plotter(res, timedelta) + sys.exit() diff --git a/a3c_cart.py b/a3c_cart.py index 4736880..ede9005 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -6,47 +6,29 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record +from utils import v_wrap, set_init, push_and_pull, record, plotter, handleArguments import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam import numpy as np import gym +import time +from datetime import datetime import sys import os os.environ["OMP_NUM_THREADS"] = "1" -import argparse -import matplotlib.pyplot as plt - UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = mp.cpu_count()*100 +MAX_EP = 5000 -env = gym.make('CartPole-v0') +env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] N_A = env.action_space.n -def plotter(): - plt.plot(res) - plt.ylabel('Average Reward') - plt.xlabel('Episode') - plt.show() - - -def handleArguments(): - """Handles CLI arguments and saves them globally""" - parser = argparse.ArgumentParser( - description="Switch between modes in A2C or loading models from previous games") - parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") - parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") - global args - args = parser.parse_args() - - class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() @@ -114,7 +96,7 @@ def run(self): buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: - if self.name == 'w00' and args.demo_mode: + if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) s_, r, done, _ = self.env.step(a) @@ -143,9 +125,11 @@ def run(self): if __name__ == "__main__": - handleArguments() # load global network - if args.load_model: + print("STARTING A3C AGENT FOR CARTPOLE-V0") + time.sleep(3) + starttime = datetime.now() + if handleArguments().load_model: gnet = Net(N_S, N_A) gnet = torch.load("./save_model/a3c_cart.pt") gnet.eval() @@ -153,7 +137,7 @@ def run(self): gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training @@ -170,12 +154,16 @@ def run(self): [w.join() for w in workers] - if global_ep_r.value >= 300: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: print("Save model") torch.save(gnet, "./save_model/a3c_cart.pt") else: print("Failed to train agent. Model was not saved") - plotter() + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + plotter(res, timedelta) + sys.exit() diff --git a/utils.py b/utils.py index 0eb56fc..ce39a3b 100644 --- a/utils.py +++ b/utils.py @@ -5,6 +5,8 @@ from torch import nn import torch import numpy as np +import matplotlib.pyplot as plt +import argparse def v_wrap(np_array, dtype=np.float32): @@ -56,8 +58,29 @@ def record(global_ep, global_ep_r, ep_r, res_queue, name): else: global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 res_queue.put(global_ep_r.value) - print( - name, - "Ep:", global_ep.value, - "| Ep_r: %.0f" % global_ep_r.value, - ) \ No newline at end of file + print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value) + + +def plotter(result, delta): + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 10, + } + plt.plot(result) + plt.text(0, 700, delta, fontdict=font) + plt.ylim(0,800) + plt.ylabel('Average Reward') + plt.xlabel('Episode') + plt.show() + + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") + parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + global args + args = parser.parse_args() + return args \ No newline at end of file From f9ace27c0655b4f7c5f2818c09f52993318e87a9 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 29 Mar 2020 10:04:53 +0200 Subject: [PATCH 08/63] CARTPOLE - if load model, then check for 100 iterations of ep_r over 500 --- a2c_cart.py | 12 +++++++++--- a2c_sync_cart.py | 11 ++++++++--- a3c_cart.py | 11 ++++++++--- 3 files changed, 25 insertions(+), 9 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 41da625..0674ad3 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -151,9 +151,15 @@ def loss_func(self, s, a, v_t): global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) scores.append(int(global_ep_r)) - if np.mean(scores[-min(10, len(scores)):]) >= 500: - stop_processes = True - break + + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and len(scores) >= 100: + stop_processes = True + break + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500: + stop_processes = True + break s = s_ total_step += 1 diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 1cada04..984bde5 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -146,9 +146,14 @@ def run(self): if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: - stop_processes = True - break + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and len(scores) >= 100: + stop_processes = True + break + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500: + stop_processes = True + break s = s_ total_step += 1 diff --git a/a3c_cart.py b/a3c_cart.py index ede9005..54e5ed9 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -115,9 +115,14 @@ def run(self): if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: - stop_processes = True - break + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and len(scores) >= 100: + stop_processes = True + break + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500: + stop_processes = True + break s = s_ total_step += 1 From 5d78d6412824198707d225df5fc690efeeb8cf4d Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 29 Mar 2020 10:15:11 +0200 Subject: [PATCH 09/63] CARTPOLE - if load_model, then check for last 100 iterations of ep_r over 500 --- a2c_cart.py | 8 ++++++-- a2c_sync_cart.py | 8 ++++++-- a3c_cart.py | 9 +++++++-- 3 files changed, 19 insertions(+), 6 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 41da625..c79ec13 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -151,8 +151,12 @@ def loss_func(self, s, a, v_t): global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) scores.append(int(global_ep_r)) - if np.mean(scores[-min(10, len(scores)):]) >= 500: - stop_processes = True + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500: + stop_processes = True break s = s_ total_step += 1 diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 1cada04..8f828cf 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -146,8 +146,12 @@ def run(self): if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: - stop_processes = True + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: + stop_processes = True break s = s_ diff --git a/a3c_cart.py b/a3c_cart.py index ede9005..f7c071a 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -115,10 +115,15 @@ def run(self): if done or ep_r == 700: # done and print information record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) scores.append(int(self.g_ep_r.value)) - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: - stop_processes = True + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: + stop_processes = True break + s = s_ total_step += 1 self.res_queue.put(None) From 93e6726b491bb62d4b89b9d6d171eff836524ae1 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 29 Mar 2020 10:26:28 +0200 Subject: [PATCH 10/63] CARTPOLE - initialize only one process when load_model --- a2c_sync_cart.py | 8 ++++++-- a3c_cart.py | 8 ++++++-- 2 files changed, 12 insertions(+), 4 deletions(-) diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 8f828cf..8262aea 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -176,8 +176,12 @@ def run(self): global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] res = [] # record episode reward to plot while True: r = res_queue.get() diff --git a/a3c_cart.py b/a3c_cart.py index f7c071a..a8d3496 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -146,8 +146,12 @@ def run(self): global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() # parallel training - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] res = [] # record episode reward to plot while True: r = res_queue.get() From 2317cea979ad40df10dbcf895a4f637d2db5af0c Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 29 Mar 2020 23:36:23 +0200 Subject: [PATCH 11/63] CARTPOLE - utils gets optimizer, a2c_sync NN update after done --- a2c_cart.py | 31 +++---------------------- a2c_sync_cart.py | 60 +++++++++++------------------------------------- utils.py | 24 +++++++++++++++++++ 3 files changed, 40 insertions(+), 75 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 2b77c67..31d8336 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, plotter, handleArguments +from utils import v_wrap, set_init, plotter, handleArguments, optimize import torch.nn.functional as F from shared_adam import SharedAdam import numpy as np @@ -28,32 +28,6 @@ N_A = env.action_space.n - -def push_and_pull(opt, gnet, done, s_, bs, ba, br, gamma): - if done: - v_s_ = 0. # terminal - else: - v_s_ = gnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] - - buffer_v_target = [] - for r in br[::-1]: # reverse buffer r - v_s_ = r + gamma * v_s_ - buffer_v_target.append(v_s_) - buffer_v_target.reverse() - - loss = gnet.loss_func( - v_wrap(np.vstack(bs)), - v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), - v_wrap(np.array(buffer_v_target)[:, None])) - - # calculate local gradients and push local parameters to global - opt.zero_grad() - loss.backward() - opt.step() - - # pull global parameters - gnet.load_state_dict(gnet.state_dict()) - class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() @@ -138,9 +112,10 @@ def loss_func(self, s, a, v_t): buffer_a.append(a) buffer_s.append(s) buffer_r.append(r) + if done or ep_r == 700: # update network # sync - push_and_pull(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] global_ep += 1 diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 8262aea..6674b30 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -19,43 +19,15 @@ os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 20 +UPDATE_GLOBAL_ITER = 50 GAMMA = 0.9 MAX_EP = 5000 - - env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] N_A = env.action_space.n -def optimize(opt, lnet, done, s_, bs, ba, br, gamma): - if done: - v_s_ = 0. # terminal - else: - v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] - - buffer_v_target = [] - for r in br[::-1]: # reverse buffer r - v_s_ = r + gamma * v_s_ - buffer_v_target.append(v_s_) - buffer_v_target.reverse() - - loss = lnet.loss_func( - v_wrap(np.vstack(bs)), - v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), - v_wrap(np.array(buffer_v_target)[:, None])) - - # calculate local gradients - opt.zero_grad() - loss.backward() - opt.step() - - #lnet.load_state_dict(lnet.state_dict()) - - class Net(nn.Module): def __init__(self, s_dim, a_dim): super(Net, self).__init__() @@ -133,26 +105,20 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 700: # update global and assign to local net + if done or ep_r == 700: # update global and assign to local net # sync - if total_step % UPDATE_GLOBAL_ITER == 0 and self.g_ep != 0: - time.sleep(1) - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + time.sleep(2) + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500: + stop_processes = True else: - optimize(self.opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - - buffer_s, buffer_a, buffer_r = [], [], [] - - if done or ep_r == 700: # done and print information - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: - stop_processes = True - break + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: + stop_processes = True + break s = s_ total_step += 1 diff --git a/utils.py b/utils.py index ce39a3b..795283a 100644 --- a/utils.py +++ b/utils.py @@ -49,6 +49,30 @@ def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): lnet.load_state_dict(gnet.state_dict()) +def optimize(opt, lnet, done, s_, bs, ba, br, gamma): + if done: + v_s_ = 0. # terminal + else: + v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + + buffer_v_target = [] + for r in br[::-1]: # reverse buffer r + # Advantage function? + v_s_ = r + gamma * v_s_ + buffer_v_target.append(v_s_) + buffer_v_target.reverse() + + loss = lnet.loss_func( + v_wrap(np.vstack(bs)), + v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), + v_wrap(np.array(buffer_v_target)[:, None])) + + # calculate local gradients + opt.zero_grad() + loss.backward() + opt.step() + + def record(global_ep, global_ep_r, ep_r, res_queue, name): with global_ep.get_lock(): global_ep.value += 1 From add50a167f73a5fd5fb6be3c3d8c935f807bcf08 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 10:07:43 +0200 Subject: [PATCH 12/63] CARTPOLE - add episode_time plotter and confidence intervall calculator for actions in utils, add information to plotter and merge, add functions in A2Cs and A3C, raise lr=0.005 --- a2c_cart.py | 174 +++++++++++++++++++++++++++-------------------- a2c_sync_cart.py | 127 ++++++++++++++++++++++------------ a3c_cart.py | 134 +++++++++++++++++++++++------------- utils.py | 61 ++++++++++++----- 4 files changed, 316 insertions(+), 180 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 31d8336..96a53b9 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -6,22 +6,20 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, plotter, handleArguments, optimize +from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt import torch.nn.functional as F from shared_adam import SharedAdam import numpy as np import gym from datetime import datetime +import time import sys import os os.environ["OMP_NUM_THREADS"] = "1" -import time - GAMMA = 0.9 -MAX_EP = 5000 - - +MAX_EP = 3000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -81,71 +79,103 @@ def loss_func(self, s, a, v_t): # load global network print ("Starting A2C Agent for Cartpole-v0") time.sleep(3) - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r = 1, 0. - - res = [] # record episode reward to plot - - name = 'w00' - total_step = 1 - stop_processes = False - scores = [] - while global_ep < MAX_EP and stop_processes is False: - s = env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - if name == 'w00' and handleArguments().demo_mode: - env.render() - a = gnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 700: # update network - # sync - optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - global_ep += 1 - - if global_ep_r == 0.: - global_ep_r = ep_r - else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) - scores.append(int(global_ep_r)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500: - stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 500: - stop_processes = True - break - - s = s_ - total_step += 1 - - if np.mean(scores[-min(10, len(scores)):]) >= 300: - print("Save model") - torch.save(gnet, "./save_model/a3c_cart.pt") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) - plotter(scores, timedelta) - + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + actions = [] + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP and stop_processes is False: + s = env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + # Initialize stopwatch for average episode duration + start = time.time() + if name == 'w00' and handleArguments().demo_mode: + env.render() + a = gnet.choose_action(v_wrap(s[None, :])) + actions.append(a) + s_, r, done, _ = env.step(a) + + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + end = time.time() + duration = end - start + durations.append(duration) + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + scores.append(int(global_ep_r)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and global_ep >= 100: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500 and global_ep >= 10: + stop_processes = True + break + + s = s_ + total_step += 1 + + if np.mean(scores[-min(10, len(scores)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart.pt") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole", fontsize = 16) + plt.show() sys.exit() diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index 6674b30..e9bc1ef 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -6,7 +6,8 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter, handleArguments +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam @@ -21,7 +22,7 @@ UPDATE_GLOBAL_ITER = 50 GAMMA = 0.9 -MAX_EP = 5000 +MAX_EP = 3000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -78,10 +79,10 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue self.gnet, self.opt = gnet, opt self.lnet = Net(N_S, N_A) # local network self.env = gym.make('CartPole-v0').unwrapped @@ -95,9 +96,11 @@ def run(self): buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: + start = time.time() if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) + self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -105,69 +108,103 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r == 700: # update global and assign to local net + if done or ep_r == 600: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - time.sleep(2) + time.sleep(0.2) + end = time.time() + time_done = end - start - 0.2 - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): stop_processes = True break s = s_ total_step += 1 self.res_queue.put(None) + self.time_queue.put(None) + self.action_queue.put(None) if __name__ == "__main__": # load global network print("Starting Synchronous A2C Agent for Cartpole-v0") time.sleep(3) - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_sync_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - res = [] # record episode reward to plot - while True: - r = res_queue.get() - if r is not None: - res.append(r) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_sync_cart.pt") + gnet.eval() else: - break + gnet = Net(N_S, N_A) - [w.join() for w in workers] + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: - print("Save model") - torch.save(gnet, "./save_model/a2c_sync_cart.pt") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - plotter(res, timedelta) + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and episode-duration to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a2c_sync_cart.pt") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - sys.exit() + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 10, + } + plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole") + plt.show() + + sys.exit() diff --git a/a3c_cart.py b/a3c_cart.py index a8d3496..feb5bb6 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -6,7 +6,8 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter, handleArguments +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp from shared_adam import SharedAdam @@ -20,9 +21,7 @@ UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 5000 - - +MAX_EP = 3000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -79,26 +78,30 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue self.gnet, self.opt = gnet, opt self.lnet = Net(N_S, N_A) # local network self.env = gym.make('CartPole-v0').unwrapped + self.time_queue, self.action_queue = time_queue, action_queue def run(self): total_step = 1 stop_processes = False scores = [] + while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: + start = time.time() if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) + self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -106,73 +109,110 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - - if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 700: # update global and assign to local net + if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] if done or ep_r == 700: # done and print information - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): stop_processes = True break - s = s_ total_step += 1 + self.time_queue.put(None) self.res_queue.put(None) + self.action_queue.put(None) if __name__ == "__main__": # load global network print("STARTING A3C AGENT FOR CARTPOLE-V0") time.sleep(3) - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - res = [] # record episode reward to plot - while True: - r = res_queue.get() - if r is not None: - res.append(r) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart.pt") + gnet.eval() else: - break + gnet = Net(N_S, N_A) + # share the global parameters in multiprocessing + gnet.share_memory() - [w.join() for w in workers] + # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: - print("Save model") - torch.save(gnet, "./save_model/a3c_cart.pt") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - plotter(res, timedelta) + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - sys.exit() + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + print("ok...") + [w.join() for w in workers] + print( "passed it") + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart.pt") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + + # Plot results after each run + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + # Finish plots + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Cartpole") + plt.show() + sys.exit() diff --git a/utils.py b/utils.py index 795283a..03b3252 100644 --- a/utils.py +++ b/utils.py @@ -6,6 +6,7 @@ import torch import numpy as np import matplotlib.pyplot as plt +import pickle import argparse @@ -73,7 +74,7 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): opt.step() -def record(global_ep, global_ep_r, ep_r, res_queue, name): +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, name): with global_ep.get_lock(): global_ep.value += 1 with global_ep_r.get_lock(): @@ -82,21 +83,49 @@ def record(global_ep, global_ep_r, ep_r, res_queue, name): else: global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 res_queue.put(global_ep_r.value) - print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value) - - -def plotter(result, delta): - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 10, - } - plt.plot(result) - plt.text(0, 700, delta, fontdict=font) - plt.ylim(0,800) - plt.ylabel('Average Reward') - plt.xlabel('Episode') - plt.show() + time_queue.put(time_done) + print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value, "| Duration:", round(time_done, 5)) + + +def plotter_ep_rew(ax2, scores): + ax2.plot(scores) + ax2.set_ylim(0,700) + ax2.set_ylabel('Reward per Episode') + ax2.set_xlabel('Episode') + + +def plotter_ep_time(ax1, duration_episode): + ax1.plot(duration_episode) + ax1.set_ylim(0,0.04) + ax1.set_ylabel('Duration of Episode') + + +def confidence_intervall(actions): + count = 0 + probab_count = 0 + probabilities = [] + for a in actions: + if a == 1: + probab_count += 1 + if count % 100 == 0 and count != 0: + probab_count = probab_count / 100 + probabilities.append(probab_count) + probab_count = 0 + count += 1 + + # Check for probabilities of actions and create confidence intervall for two standard deviations + print("Probabilities: ", probabilities) + + stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 + print ("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) + + stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", + round(probabilities[2] + stan_dev2, 3)) + + stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) / 100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + round(probabilities[3] + stan_dev3, 3)) def handleArguments(): From b9ec460af3ccae83d1e3f585463749d9f44f6b13 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 10:08:46 +0200 Subject: [PATCH 13/63] CARTPOLE - create A2Cs and A3C shared NN --- a2c_cart_comb.py | 179 ++++++++++++++++++++++++++++++++++ a2c_sync_cart_comb.py | 208 +++++++++++++++++++++++++++++++++++++++ a3c_cart_comb.py | 221 ++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 608 insertions(+) create mode 100644 a2c_cart_comb.py create mode 100644 a2c_sync_cart_comb.py create mode 100644 a3c_cart_comb.py diff --git a/a2c_cart_comb.py b/a2c_cart_comb.py new file mode 100644 index 0000000..793ac5c --- /dev/null +++ b/a2c_cart_comb.py @@ -0,0 +1,179 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +from shared_adam import SharedAdam +import numpy as np +import gym +from datetime import datetime +import time +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v2 = nn.Linear(48, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +if __name__ == "__main__": + # load global network + print ("Starting A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + actions = [] + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP and stop_processes is False: + s = env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + # Initialize stopwatch for average episode duration + start = time.time() + if name == 'w00' and handleArguments().demo_mode: + env.render() + a = gnet.choose_action(v_wrap(s[None, :])) + actions.append(a) + s_, r, done, _ = env.step(a) + + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + end = time.time() + duration = end - start + durations.append(duration) + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + scores.append(int(global_ep_r)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and global_ep >= 100: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 500 and global_ep >= 10: + stop_processes = True + break + + s = s_ + total_step += 1 + + if np.mean(scores[-min(10, len(scores)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a2c_cart_comb.pt") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize = 16) + plt.show() + + sys.exit() diff --git a/a2c_sync_cart_comb.py b/a2c_sync_cart_comb.py new file mode 100644 index 0000000..a6d1528 --- /dev/null +++ b/a2c_sync_cart_comb.py @@ -0,0 +1,208 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + + +UPDATE_GLOBAL_ITER = 50 +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v2 = nn.Linear(48, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + self.action_queue.put(a) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + time.sleep(0.2) + end = time.time() + time_done = end - start - 0.2 + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + self.res_queue.put(None) + self.time_queue.put(None) + self.action_queue.put(None) + + +if __name__ == "__main__": + # load global network + print("Starting Synchronous A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_sync_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and episode-duration to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a2c_sync_cart_comb.pt") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 10, + } + plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole (shared NN)") + plt.show() + + sys.exit() + diff --git a/a3c_cart_comb.py b/a3c_cart_comb.py new file mode 100644 index 0000000..610a3bc --- /dev/null +++ b/a3c_cart_comb.py @@ -0,0 +1,221 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 20 +GAMMA = 0.9 +MAX_EP = 3000 + + + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v2 = nn.Linear(48, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + self.time_queue, self.action_queue = time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + self.action_queue.put(a) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + + if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + if done or ep_r == 700: # done and print information + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + + + break + + + s = s_ + total_step += 1 + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == "__main__": + # load global network + print("STARTING A3C AGENT FOR CARTPOLE-V0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + # share the global parameters in multiprocessing + gnet.share_memory() + + # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) + + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart_comb.pt") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + confidence_intervall(actions) + + # Plot results after each run + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + # Finish plots + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Cartpole (shared NN)") + plt.show() + + sys.exit() + From e35afd86f1cfe1be7eea99b40a2b13530aa27b0c Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 11:13:32 +0200 Subject: [PATCH 14/63] CARTPOLE - fix confidence interval error in Sync_A2C and A3C, action_queue in record function --- a2c_sync_cart.py | 3 +-- a2c_sync_cart_comb.py | 3 +-- a3c_cart.py | 14 ++++++-------- a3c_cart_comb.py | 3 +-- utils.py | 3 ++- 5 files changed, 11 insertions(+), 15 deletions(-) diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py index e9bc1ef..a577e69 100644 --- a/a2c_sync_cart.py +++ b/a2c_sync_cart.py @@ -100,7 +100,6 @@ def run(self): if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) - self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -115,7 +114,7 @@ def run(self): end = time.time() time_done = end - start - 0.2 - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: diff --git a/a2c_sync_cart_comb.py b/a2c_sync_cart_comb.py index a6d1528..c484c6c 100644 --- a/a2c_sync_cart_comb.py +++ b/a2c_sync_cart_comb.py @@ -98,7 +98,6 @@ def run(self): if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) - self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -113,7 +112,7 @@ def run(self): end = time.time() time_done = end - start - 0.2 - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: diff --git a/a3c_cart.py b/a3c_cart.py index feb5bb6..6d1c1aa 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -101,7 +101,6 @@ def run(self): if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) - self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -117,8 +116,9 @@ def run(self): if done or ep_r == 700: # done and print information end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: stop_processes = True @@ -131,7 +131,6 @@ def run(self): total_step += 1 self.time_queue.put(None) self.res_queue.put(None) - self.action_queue.put(None) if __name__ == "__main__": @@ -171,7 +170,7 @@ def run(self): # record episode-reward and duration-episode to plot res = [] durations = [] - actions = [] + action = [] while True: r = res_queue.get() t = time_queue.get() @@ -179,13 +178,11 @@ def run(self): if r is not None: res.append(r) durations.append(t) - actions.append(a) + action.append(a) else: break - print("ok...") [w.join() for w in workers] - print( "passed it") if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: print("Save model") torch.save(gnet, "./save_model/a3c_cart.pt") @@ -198,7 +195,8 @@ def run(self): timedelta_sum += timedelta/3 # Get results for confidence intervall - confidence_intervall(actions) + print(action) + confidence_intervall(action) # Plot results after each run plotter_ep_time(ax1, durations) diff --git a/a3c_cart_comb.py b/a3c_cart_comb.py index 610a3bc..d42f697 100644 --- a/a3c_cart_comb.py +++ b/a3c_cart_comb.py @@ -101,7 +101,6 @@ def run(self): if self.name == 'w00' and handleArguments().demo_mode: self.env.render() a = self.lnet.choose_action(v_wrap(s[None, :])) - self.action_queue.put(a) s_, r, done, _ = self.env.step(a) if done: r = -1 ep_r += r @@ -118,7 +117,7 @@ def run(self): if done or ep_r == 700: # done and print information end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: diff --git a/utils.py b/utils.py index 03b3252..bfdcc90 100644 --- a/utils.py +++ b/utils.py @@ -74,7 +74,7 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): opt.step() -def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, name): +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, action_queue, name): with global_ep.get_lock(): global_ep.value += 1 with global_ep_r.get_lock(): @@ -84,6 +84,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, name) global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 res_queue.put(global_ep_r.value) time_queue.put(time_done) + action_queue.put(a) print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value, "| Duration:", round(time_done, 5)) From 6d3212214ba4e9a4520b332e0d0a8836497798a3 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 14:52:52 +0200 Subject: [PATCH 15/63] CARTPOLE - refactor shared network algorithms, delete one confidence intervall calculation --- a2c_cart_comb.py | 13 ++++++++----- a2c_sync_cart_comb.py | 13 ++++++++----- a3c_cart_comb.py | 14 ++++++++------ utils.py | 5 +---- 4 files changed, 25 insertions(+), 20 deletions(-) diff --git a/a2c_cart_comb.py b/a2c_cart_comb.py index 793ac5c..6254a25 100644 --- a/a2c_cart_comb.py +++ b/a2c_cart_comb.py @@ -31,22 +31,25 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) + self.pi1 = nn.Linear(s_dim, 24) + self.pi12 = nn.Linear(24, 48) self.pi2 = nn.Linear(48, 24) self.pi3 = nn.Linear(24, a_dim) self.v2 = nn.Linear(48, 24) self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) + pi12 = F.relu(self.pi12(pi1)) + pi2 = F.relu(self.pi2(pi12)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi1)) + v2 = F.relu(self.v2(pi12)) values = self.v3(v2) return logits, values + def set_init(layers): for layer in layers: nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) @@ -92,7 +95,7 @@ def loss_func(self, s, a, v_t): else: gnet = Net(N_S, N_A) - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer # Global variables for episodes durations = [] diff --git a/a2c_sync_cart_comb.py b/a2c_sync_cart_comb.py index c484c6c..838280a 100644 --- a/a2c_sync_cart_comb.py +++ b/a2c_sync_cart_comb.py @@ -34,22 +34,25 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) + self.pi1 = nn.Linear(s_dim, 24) + self.pi12 = nn.Linear(24, 48) self.pi2 = nn.Linear(48, 24) self.pi3 = nn.Linear(24, a_dim) self.v2 = nn.Linear(48, 24) self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) + pi12 = F.relu(self.pi12(pi1)) + pi2 = F.relu(self.pi2(pi12)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi1)) + v2 = F.relu(self.v2(pi12)) values = self.v3(v2) return logits, values + def set_init(layers): for layer in layers: nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) @@ -148,7 +151,7 @@ def run(self): gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() # parallel training diff --git a/a3c_cart_comb.py b/a3c_cart_comb.py index d42f697..c20aed1 100644 --- a/a3c_cart_comb.py +++ b/a3c_cart_comb.py @@ -35,19 +35,21 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) + self.pi1 = nn.Linear(s_dim, 24) + self.pi12 = nn.Linear(24, 48) self.pi2 = nn.Linear(48, 24) self.pi3 = nn.Linear(24, a_dim) self.v2 = nn.Linear(48, 24) self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) + pi12 = F.relu(self.pi12(pi1)) + pi2 = F.relu(self.pi2(pi12)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi1)) + v2 = F.relu(self.v2(pi12)) values = self.v3(v2) return logits, values @@ -114,7 +116,7 @@ def run(self): push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] - if done or ep_r == 700: # done and print information + if done or ep_r == 600: # done and print information end = time.time() time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) @@ -159,7 +161,7 @@ def run(self): gnet.share_memory() # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) + opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() diff --git a/utils.py b/utils.py index bfdcc90..9bc2ea5 100644 --- a/utils.py +++ b/utils.py @@ -90,6 +90,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, ac def plotter_ep_rew(ax2, scores): ax2.plot(scores) + ax2.axhline(y=200.00, color='r') ax2.set_ylim(0,700) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') @@ -124,10 +125,6 @@ def confidence_intervall(actions): print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", round(probabilities[2] + stan_dev2, 3)) - stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) / 100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", - round(probabilities[3] + stan_dev3, 3)) - def handleArguments(): """Handles CLI arguments and saves them globally""" From 211ce33cbb32862a678f3bf287342a19cfe1e577 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 19:43:34 +0200 Subject: [PATCH 16/63] CARTPOLE - refactor shared NNs and lr, take out time sleep in test phase for Sync-A2C --- a2c_cart.py | 15 ++++++++----- a2c_cart_comb.py | 15 ++++++++----- a2c_sync_cart_comb.py | 46 +++++++++++++++++++++------------------- a3c_cart.py | 49 +++++++++++++++++++++++-------------------- a3c_cart_comb.py | 37 ++++++++++++++++---------------- utils.py | 26 ++++++++++++++++------- 6 files changed, 107 insertions(+), 81 deletions(-) diff --git a/a2c_cart.py b/a2c_cart.py index 96a53b9..3eae5c7 100644 --- a/a2c_cart.py +++ b/a2c_cart.py @@ -142,19 +142,21 @@ def loss_func(self, s, a, v_t): print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) scores.append(int(global_ep_r)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and global_ep >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True else: - if np.mean(scores[-min(10, len(scores)):]) >= 500 and global_ep >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True break s = s_ total_step += 1 - if np.mean(scores[-min(10, len(scores)):]) >= 300: + if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a3c_cart.pt") + torch.save(gnet, "./save_model/a2c_cart.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") endtime = datetime.now() @@ -164,7 +166,10 @@ def loss_func(self, s, a, v_t): timedelta_sum += timedelta/3 # Get results for confidence intervall - confidence_intervall(actions) + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) # Plot results plotter_ep_time(ax1, durations) diff --git a/a2c_cart_comb.py b/a2c_cart_comb.py index 6254a25..23ece2f 100644 --- a/a2c_cart_comb.py +++ b/a2c_cart_comb.py @@ -95,7 +95,7 @@ def loss_func(self, s, a, v_t): else: gnet = Net(N_S, N_A) - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer # Global variables for episodes durations = [] @@ -143,19 +143,21 @@ def loss_func(self, s, a, v_t): print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) scores.append(int(global_ep_r)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and global_ep >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True else: - if np.mean(scores[-min(10, len(scores)):]) >= 500 and global_ep >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True break s = s_ total_step += 1 - if np.mean(scores[-min(10, len(scores)):]) >= 300: + if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") torch.save(gnet, "./save_model/a2c_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") endtime = datetime.now() @@ -165,7 +167,10 @@ def loss_func(self, s, a, v_t): timedelta_sum += timedelta/3 # Get results for confidence intervall - confidence_intervall(actions) + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) # Plot results plotter_ep_time(ax1, durations) diff --git a/a2c_sync_cart_comb.py b/a2c_sync_cart_comb.py index 838280a..68d259d 100644 --- a/a2c_sync_cart_comb.py +++ b/a2c_sync_cart_comb.py @@ -19,8 +19,6 @@ import os os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 50 GAMMA = 0.9 MAX_EP = 3000 @@ -34,21 +32,19 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi12 = nn.Linear(24, 48) - self.pi2 = nn.Linear(48, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v2 = nn.Linear(48, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 48) + self.pi3 = nn.Linear(48, a_dim) + self.v2 = nn.Linear(48, 48) + self.v3 = nn.Linear(48, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi12 = F.relu(self.pi12(pi1)) - pi2 = F.relu(self.pi2(pi12)) + pi2 = F.relu(self.pi2(pi1)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi12)) + v2 = F.relu(self.v2(pi1)) values = self.v3(v2) return logits, values @@ -111,17 +107,20 @@ def run(self): if done or ep_r == 600: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - time.sleep(0.2) + end = time.time() - time_done = end - start - 0.2 + + if not handleArguments().load_model: + time.sleep(0.5) + time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True break @@ -151,7 +150,7 @@ def run(self): gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() # parallel training @@ -179,9 +178,11 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") torch.save(gnet, "./save_model/a2c_sync_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") endtime = datetime.now() @@ -191,7 +192,10 @@ def run(self): timedelta_sum += timedelta/3 # Get results for confidence intervall - confidence_intervall(actions) + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) # Plot results plotter_ep_time(ax1, durations) @@ -200,10 +204,10 @@ def run(self): font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', - 'size': 10, + 'size': 8, } plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole (shared NN)") + plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize = 16) plt.show() sys.exit() diff --git a/a3c_cart.py b/a3c_cart.py index 6d1c1aa..ec59c1d 100644 --- a/a3c_cart.py +++ b/a3c_cart.py @@ -19,7 +19,7 @@ import os os.environ["OMP_NUM_THREADS"] = "1" -UPDATE_GLOBAL_ITER = 20 +#UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 MAX_EP = 3000 @@ -108,30 +108,29 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net + if done or ep_r == 600: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - if done or ep_r == 700: # done and print information - end = time.time() - time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break + end = time.time() + time_done = end - start + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break s = s_ total_step += 1 self.time_queue.put(None) self.res_queue.put(None) - + self.action_queue.put(None) if __name__ == "__main__": # load global network @@ -170,7 +169,7 @@ def run(self): # record episode-reward and duration-episode to plot res = [] durations = [] - action = [] + actions = [] while True: r = res_queue.get() t = time_queue.get() @@ -178,14 +177,16 @@ def run(self): if r is not None: res.append(r) durations.append(t) - action.append(a) + actions.append(a) else: break [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") torch.save(gnet, "./save_model/a3c_cart.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") @@ -195,8 +196,10 @@ def run(self): timedelta_sum += timedelta/3 # Get results for confidence intervall - print(action) - confidence_intervall(action) + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) # Plot results after each run plotter_ep_time(ax1, durations) diff --git a/a3c_cart_comb.py b/a3c_cart_comb.py index c20aed1..bc4b1b8 100644 --- a/a3c_cart_comb.py +++ b/a3c_cart_comb.py @@ -35,21 +35,19 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi12 = nn.Linear(24, 48) - self.pi2 = nn.Linear(48, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v2 = nn.Linear(48, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 48) + self.pi3 = nn.Linear(48, a_dim) + self.v2 = nn.Linear(48, 48) + self.v3 = nn.Linear(48, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi12 = F.relu(self.pi12(pi1)) - pi2 = F.relu(self.pi2(pi12)) + pi2 = F.relu(self.pi2(pi1)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi12)) + v2 = F.relu(self.v2(pi1)) values = self.v3(v2) return logits, values @@ -122,16 +120,12 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True - - break - - s = s_ total_step += 1 self.time_queue.put(None) @@ -161,7 +155,7 @@ def run(self): gnet.share_memory() # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.003, betas=(0.92, 0.999)) + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() @@ -190,9 +184,11 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") torch.save(gnet, "./save_model/a3c_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") @@ -202,7 +198,10 @@ def run(self): timedelta_sum += timedelta/3 # Get results for confidence intervall - confidence_intervall(actions) + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) # Plot results after each run plotter_ep_time(ax1, durations) diff --git a/utils.py b/utils.py index 9bc2ea5..e42fde7 100644 --- a/utils.py +++ b/utils.py @@ -102,14 +102,14 @@ def plotter_ep_time(ax1, duration_episode): ax1.set_ylabel('Duration of Episode') -def confidence_intervall(actions): - count = 0 +def confidence_intervall(actions, load_model = False): + count = 1 probab_count = 0 probabilities = [] for a in actions: if a == 1: probab_count += 1 - if count % 100 == 0 and count != 0: + if count % 100 == 0 and count != 1: probab_count = probab_count / 100 probabilities.append(probab_count) probab_count = 0 @@ -118,12 +118,22 @@ def confidence_intervall(actions): # Check for probabilities of actions and create confidence intervall for two standard deviations print("Probabilities: ", probabilities) - stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 - print ("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) + if load_model == True: + stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 + print("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", + round(probabilities[0] - stan_dev1, 3), " and", round(probabilities[0] + stan_dev1, 3)) - stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", - round(probabilities[2] + stan_dev2, 3)) + else: + stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 + print ("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) + + stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", + round(probabilities[2] + stan_dev2, 3)) + + stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + round(probabilities[3] + stan_dev3, 3)) def handleArguments(): From aca0f930dbe86717a0c1bc324f06eb6699e76d90 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Wed, 8 Apr 2020 19:46:25 +0200 Subject: [PATCH 17/63] CARTPOLE - results and saved models --- save_model/a2c_cart_comb.pt | Bin 0 -> 21893 bytes save_model/a2c_sync_cart.pt | Bin 0 -> 12951 bytes 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z92E661YSdL3q1~Z7L1(m#sth_hvVNh!3qt#s6lwH{6GB0k#%;4C>-}4NI$Ogy$3qG zL|#*TL4ywQ50FdU|?IO-*a-1Nb)x+abDo5%)vf>X0~rMD7>}brKDl5;eFYY_ zP9n6l`zUlz#M5w4oR)hfwX{UJxNIPa4ItZ{{e9C;+yfXzRW&vG;+%5G`{L&IX+IwBTK!>;NGwB=wa*0V5#HEWAj+ zA}Rrkl&UQODuCBJkXBd9PK2fphISaS5S88-Zx9W4y%7tHdH+5qBct4`?+%tepsih& zGBlHw<>heJfq7fe*xdYwIR13w9+e~^>E)E?5poGHJ-sRFSg_1&#sSVYtKWGfDih4h z8Pac~b|CYyN8&il-2r?$z$6zHS*_GV1%Bb4A0UnkhPR7}X~#kz2d6Zg!I&5r8zbeI zcULb`=g79erbR98iq@A0hAOD~gkOG4O2SLOko%e|*T*M-U-Bt2cju$0_yw%T&5ts05@);>$Mf+Ncq-p z(37nU Date: Fri, 10 Apr 2020 01:37:21 +0200 Subject: [PATCH 19/63] VIZDOOM - create A2C-vizdoom for deadly corridor, create viz_utils --- VIZDOOM/vizdoom_a2c.py | 218 +++++++++++++++++++++++++++++++++++++++++ viz_utils.py | 151 ++++++++++++++++++++++++++++ 2 files changed, 369 insertions(+) create mode 100644 VIZDOOM/vizdoom_a2c.py create mode 100644 viz_utils.py diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py new file mode 100644 index 0000000..7d23035 --- /dev/null +++ b/VIZDOOM/vizdoom_a2c.py @@ -0,0 +1,218 @@ +from absl import app, flags +import itertools +from random import sample, randint, random +import numpy as np +from skimage.transform import resize +from time import time, sleep +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from shared_adam import SharedAdam +import gym + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +GAMMA = 0.9 +MAX_EP = 30 +FLAGS = flags.FLAGS +frame_repeat = 12 +resolution = (30, 45) +default_config_file_path = "/home/juice100/pytorch-A3C/VIZDOOM/deadly_corridor.cfg" + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + game.set_window_visible(True) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + + +class QNet(nn.Module): + def __init__(self, a_dim): + super(QNet, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 120) + self.pi2 = nn.Linear(120, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v1 = nn.Linear(self.s_dim, 120) + self.v2 = nn.Linear(120, 360) + self.v3 = nn.Linear(360, 1) + + #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) + + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + def train_step(self, s1, target_q): + output = self(s1) + loss = self.criterion(output, target_q) + self.optimizer.zero_grad() + loss.backward() + self.optimizer.step() + return loss + + def learn_from_memory(self): + if self.memory.size < FLAGS.batch_size: return + s1, a, s2, isterminal, r = self.memory.get_sample(FLAGS.batch_size) + q = self(s2).detach() + q2, _ = torch.max(q, dim=1) + target_q = self(s1).detach() + idxs = (torch.arange(target_q.shape[0]), a) + target_q[idxs] = r + FLAGS.discount * (1-isterminal) * q2 + self.train_step(s1, target_q) + + +def main(_): + + game = initialize_vizdoom(FLAGS.config) + statesize = (game_state(game).shape[0]) + state=game_state(game) + print("Current State:" , state, "\n") + print("Statesize:" , statesize, "\n") + n = game.get_available_buttons_size() + print ("Action Size: ", n) + actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + + + model = QNet(len(actions)) + opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + + while global_ep < MAX_EP: + game.new_episode() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + + done = False + a = model.choose_action(state) + actions.append(a) + #s_, r, done, _ = env.step(a) + + r = game.make_action(actions[a], frame_repeat) + #print("Reward:", r) + + if game.is_episode_finished(): + done = True + else: + s_ = game_state(game) + + #if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + game.get_total_reward() + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) + break + + s = s_ + total_step += 1 + + game.close() + +if __name__ == '__main__': + flags.DEFINE_integer('batch_size', 64, 'Batch size') + flags.DEFINE_float('learning_rate', 0.00025, 'Learning rate') + flags.DEFINE_float('discount', 0.99, 'Discount factor') + flags.DEFINE_integer('replay_memory', 10000, 'Replay memory capacity') + flags.DEFINE_integer('epochs', 20, 'Number of epochs') + flags.DEFINE_integer('iters', 2000, 'Iterations per epoch') + flags.DEFINE_integer('watch_episodes', 10, 'Trained episodes to watch') + flags.DEFINE_integer('test_episodes', 100, 'Episodes to test with') + flags.DEFINE_string('config', default_config_file_path, + 'Path to the config file') + flags.DEFINE_boolean('skip_training', False, 'Set to skip training') + flags.DEFINE_boolean('load_model', False, 'Load the model from disk') + flags.DEFINE_string('save_path', 'model-doom.pth', + 'Path to save/load the model') + app.run(main) + + diff --git a/viz_utils.py b/viz_utils.py new file mode 100644 index 0000000..923f98b --- /dev/null +++ b/viz_utils.py @@ -0,0 +1,151 @@ +""" +Functions that use multiple times +""" + +from torch import nn +import torch +import numpy as np +import matplotlib.pyplot as plt +import pickle +import argparse + + +def v_wrap(np_array, dtype=np.float32): + if np_array.dtype != dtype: + np_array = np_array.astype(dtype) + return torch.from_numpy(np_array) + + +def set_init(layers): + for layer in layers: + nn.init.normal_(layer.weight, mean=0., std=0.1) + nn.init.constant_(layer.bias, 0.) + + +def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): + if done: + v_s_ = 0. # terminal + else: + v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + + + buffer_v_target = [] + for r in br[::-1]: # reverse buffer r + v_s_ = r + gamma * v_s_ + buffer_v_target.append(v_s_) + buffer_v_target.reverse() + + loss = lnet.loss_func( + v_wrap(np.vstack(bs)), + v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), + v_wrap(np.array(buffer_v_target)[:, None])) + + + # calculate local gradients and push local parameters to global + opt.zero_grad() + loss.backward() + for lp, gp in zip(lnet.parameters(), gnet.parameters()): + gp._grad = lp.grad + opt.step() + + # pull global parameters + lnet.load_state_dict(gnet.state_dict()) + + +def optimize(opt, lnet, done, s_, bs, ba, br, gamma): + if done: + v_s_ = 0. # terminal + else: + v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + + buffer_v_target = [] + + for r in br[::-1]: # reverse buffer r + # Advantage function? + v_s_ = r + gamma * v_s_ + buffer_v_target.append(v_s_) + buffer_v_target.reverse() + + loss = lnet.loss_func( + v_wrap(np.vstack(bs)), + v_wrap(np.array(ba), dtype=np.int64) if ba[0].dtype == np.int64 else v_wrap(np.vstack(ba)), + v_wrap(np.array(buffer_v_target)[:, None])) + # calculate local gradients + opt.zero_grad() + loss.backward() + opt.step() + + +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, action_queue, name): + with global_ep.get_lock(): + global_ep.value += 1 + with global_ep_r.get_lock(): + if global_ep_r.value == 0.: + global_ep_r.value = ep_r + else: + global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 + res_queue.put(global_ep_r.value) + time_queue.put(time_done) + action_queue.put(a) + print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value, "| Duration:", round(time_done, 5)) + + +def plotter_ep_rew(ax2, scores): + ax2.plot(scores) + ax2.axhline(y=200.00, color='r') + ax2.set_ylim(0,700) + ax2.set_ylabel('Reward per Episode') + ax2.set_xlabel('Episode') + + +def plotter_ep_time(ax1, duration_episode): + ax1.plot(duration_episode) + ax1.set_ylim(0,0.04) + ax1.set_ylabel('Duration of Episode') + + +def confidence_intervall(actions, load_model = False): + count = 1 + probab_count = 0 + probabilities = [] + for a in actions: + if a == 1: + probab_count += 1 + if count % 100 == 0 and count != 1: + probab_count = probab_count / 100 + probabilities.append(probab_count) + probab_count = 0 + count += 1 + + # Check for probabilities of actions and create confidence intervall for two standard deviations + print("Probabilities: ", probabilities) + + if load_model == True: + stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 + print("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", + round(probabilities[0] - stan_dev1, 3), " and", round(probabilities[0] + stan_dev1, 3)) + + else: + stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 + print ("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) + + stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", + round(probabilities[2] + stan_dev2, 3)) + + stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + round(probabilities[3] + stan_dev3, 3)) + + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") + parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + global args + args = parser.parse_args() + return args + + From 671f0577f22b07f904431c7d78f87a629485c045 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 10 Apr 2020 01:44:42 +0200 Subject: [PATCH 20/63] CARTPOLE - create directory --- CARTPOLE/a2c_cart.py | 188 +++++++++++++++ CARTPOLE/a2c_cart_comb.py | 187 +++++++++++++++ CARTPOLE/a2c_sync_cart.py | 217 +++++++++++++++++ CARTPOLE/a2c_sync_cart_comb.py | 214 +++++++++++++++++ CARTPOLE/a3c_cart.py | 219 +++++++++++++++++ CARTPOLE/a3c_cart_comb.py | 221 ++++++++++++++++++ .../A2C Performance Summary | 11 + .../A2C-Sync Performance Summary | 5 + .../A2C_Cart/A2C_cart__shared_train.png | Bin 0 -> 52833 bytes .../A2C_Cart/A2C_cart_shared_test.png | Bin 0 -> 54786 bytes .../A2C_Cart/A2C_cart_test.png | Bin 0 -> 38018 bytes .../A2C_Cart/A2C_cart_train.png | Bin 0 -> 54174 bytes .../A2C_cart_sync_shared_test.png | Bin 0 -> 39810 bytes .../A2C_cart_sync_shared_train.png | Bin 0 -> 41995 bytes 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100644 CARTPOLE/a2c_sync_cart_comb.py create mode 100644 CARTPOLE/a3c_cart.py create mode 100644 CARTPOLE/a3c_cart_comb.py create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart__shared_train.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_shared_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_train.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_train.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train2.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C Performance Summary create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_shared.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_shared_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_train.png create mode 100644 CARTPOLE/cart_save_model/a2c_cart_comb.pt create mode 100644 CARTPOLE/cart_save_model/a2c_sync_cart.pt create mode 100644 CARTPOLE/cart_save_model/a2c_sync_cart_comb.pt create mode 100644 CARTPOLE/cart_save_model/a3c_cart.pt create mode 100644 CARTPOLE/cart_save_model/a3c_cart_comb.pt create mode 100644 CARTPOLE/cart_utils.py diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py new file mode 100644 index 0000000..2c19230 --- /dev/null +++ b/CARTPOLE/a2c_cart.py @@ -0,0 +1,188 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +from shared_adam import SharedAdam +import numpy as np +import gym +from datetime import datetime +import time +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + print ("V_T:", v_t) + print ("Values:", values) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +if __name__ == "__main__": + # load global network + print ("Starting A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + actions = [] + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP and stop_processes is False: + s = env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + # Initialize stopwatch for average episode duration + start = time.time() + if name == 'w00' and handleArguments().demo_mode: + env.render() + a = gnet.choose_action(v_wrap(s[None, :])) + actions.append(a) + s_, r, done, _ = env.step(a) + + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + end = time.time() + duration = end - start + durations.append(duration) + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + scores.append(int(global_ep_r)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: + stop_processes = True + break + + s = s_ + total_step += 1 + + if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a2c_cart.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole", fontsize = 16) + plt.show() + + sys.exit() diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py new file mode 100644 index 0000000..23ece2f --- /dev/null +++ b/CARTPOLE/a2c_cart_comb.py @@ -0,0 +1,187 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +from shared_adam import SharedAdam +import numpy as np +import gym +from datetime import datetime +import time +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 24) + self.pi12 = nn.Linear(24, 48) + self.pi2 = nn.Linear(48, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v2 = nn.Linear(48, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi12 = F.relu(self.pi12(pi1)) + pi2 = F.relu(self.pi2(pi12)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi12)) + values = self.v3(v2) + return logits, values + + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +if __name__ == "__main__": + # load global network + print ("Starting A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + actions = [] + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP and stop_processes is False: + s = env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + # Initialize stopwatch for average episode duration + start = time.time() + if name == 'w00' and handleArguments().demo_mode: + env.render() + a = gnet.choose_action(v_wrap(s[None, :])) + actions.append(a) + s_, r, done, _ = env.step(a) + + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + end = time.time() + duration = end - start + durations.append(duration) + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + scores.append(int(global_ep_r)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: + stop_processes = True + break + + s = s_ + total_step += 1 + + if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a2c_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize = 16) + plt.show() + + sys.exit() diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py new file mode 100644 index 0000000..900cdb7 --- /dev/null +++ b/CARTPOLE/a2c_sync_cart.py @@ -0,0 +1,217 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + + +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + + end = time.time() + + if not handleArguments().load_model: + time.sleep(0.5) + time_done = end - start + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + self.res_queue.put(None) + self.time_queue.put(None) + self.action_queue.put(None) + + +if __name__ == "__main__": + # load global network + print("Starting Synchronous A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_sync_cart.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and episode-duration to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a2c_sync_cart.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole", fontsize = 16) + plt.show() + + sys.exit() + diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py new file mode 100644 index 0000000..68d259d --- /dev/null +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -0,0 +1,214 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 48) + self.pi3 = nn.Linear(48, a_dim) + self.v2 = nn.Linear(48, 48) + self.v3 = nn.Linear(48, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + + end = time.time() + + if not handleArguments().load_model: + time.sleep(0.5) + time_done = end - start + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + self.res_queue.put(None) + self.time_queue.put(None) + self.action_queue.put(None) + + +if __name__ == "__main__": + # load global network + print("Starting Synchronous A2C Agent for Cartpole-v0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a2c_sync_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and episode-duration to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a2c_sync_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize = 16) + plt.show() + + sys.exit() + diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py new file mode 100644 index 0000000..ec59c1d --- /dev/null +++ b/CARTPOLE/a3c_cart.py @@ -0,0 +1,219 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +#UPDATE_GLOBAL_ITER = 20 +GAMMA = 0.9 +MAX_EP = 3000 + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 24) + self.pi2 = nn.Linear(24, 24) + self.pi3 = nn.Linear(24, a_dim) + self.v1 = nn.Linear(s_dim, 24) + self.v2 = nn.Linear(24, 24) + self.v3 = nn.Linear(24, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + self.time_queue, self.action_queue = time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + if done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + end = time.time() + time_done = end - start + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + +if __name__ == "__main__": + # load global network + print("STARTING A3C AGENT FOR CARTPOLE-V0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + # share the global parameters in multiprocessing + gnet.share_memory() + + # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) + + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results after each run + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + # Finish plots + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Cartpole") + plt.show() + + sys.exit() + diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py new file mode 100644 index 0000000..bc4b1b8 --- /dev/null +++ b/CARTPOLE/a3c_cart_comb.py @@ -0,0 +1,221 @@ +""" +Reinforcement Learning (A3C) using Pytroch + multiprocessing. +The most simple implementation for continuous action. + +""" + +import torch +import torch.nn as nn +from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +import matplotlib.pyplot as plt +import torch.nn.functional as F +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import numpy as np +import gym +import time +from datetime import datetime +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 20 +GAMMA = 0.9 +MAX_EP = 3000 + + + +env = gym.make('CartPole-v0').unwrapped +N_S = env.observation_space.shape[0] +N_A = env.action_space.n + + +class Net(nn.Module): + def __init__(self, s_dim, a_dim): + super(Net, self).__init__() + self.s_dim = s_dim + self.a_dim = a_dim + self.pi1 = nn.Linear(s_dim, 48) + self.pi2 = nn.Linear(48, 48) + self.pi3 = nn.Linear(48, a_dim) + self.v2 = nn.Linear(48, 48) + self.v3 = nn.Linear(48, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + td = v_t - values + c_loss = td.pow(2) + + probs = F.softmax(logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(N_S, N_A) # local network + self.env = gym.make('CartPole-v0').unwrapped + self.time_queue, self.action_queue = time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + s = self.env.reset() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + if self.name == 'w00' and handleArguments().demo_mode: + self.env.render() + a = self.lnet.choose_action(v_wrap(s[None, :])) + s_, r, done, _ = self.env.step(a) + if done: r = -1 + ep_r += r + buffer_a.append(a) + buffer_s.append(s) + buffer_r.append(r) + + + if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net + # sync + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + buffer_s, buffer_a, buffer_r = [], [], [] + + if done or ep_r == 600: # done and print information + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + s = s_ + total_step += 1 + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == "__main__": + # load global network + print("STARTING A3C AGENT FOR CARTPOLE-V0") + time.sleep(3) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Reinitialize Agent to make 5 trials + for i in range(3): + starttime = datetime.now() + if handleArguments().load_model: + gnet = Net(N_S, N_A) + gnet = torch.load("./save_model/a3c_cart_comb.pt") + gnet.eval() + else: + gnet = Net(N_S, N_A) + + # share the global parameters in multiprocessing + gnet.share_memory() + + # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) + + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + actions = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + actions.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + print("Save model") + torch.save(gnet, "./save_model/a3c_cart_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + + # Plot results after each run + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + # Finish plots + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8, + } + plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Cartpole (shared NN)") + plt.show() + + sys.exit() + diff --git a/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary b/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary new file mode 100644 index 0000000..04f552f --- /dev/null +++ b/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary @@ -0,0 +1,11 @@ +A2C Cartpole Performance: + +- Most stable algorithm with episode length of about 0.005 to 0.01 seconds +- Confidence of 95% to have action "right" between 40% and 60% in average, tendency to right as 43%-63% up to 47%-67% also appeared +- Test run performs stable above average reward of 500 +- Breaches stability threshold at about episode 300 to 600 + +Shared NN: + +- Slighty weaker as clearing comes after 1500 episodes (doesn't always clear within 3000 episodes) +- Breaches stability threshold after 500-1000 episodes diff --git a/CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary b/CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary new file mode 100644 index 0000000..38e50cd --- /dev/null +++ b/CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary @@ -0,0 +1,5 @@ +A2C Cartpole Performance: + +- Low performing, but stable algorithm with episode length of about 0.007 to 0.01 seconds +- Confidence of 95% to have action 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151 ++++++++++++++++++++++++++++++++++++ VIZDOOM/vizdoom_a2c.py | 2 +- 4 files changed, 195 insertions(+), 1 deletion(-) create mode 100644 VIZDOOM/deadly_corridor.cfg create mode 100644 VIZDOOM/deadly_corridor.wad create mode 100644 VIZDOOM/viz_utils.py diff --git a/VIZDOOM/deadly_corridor.cfg b/VIZDOOM/deadly_corridor.cfg new file mode 100644 index 0000000..d2e34d4 --- /dev/null +++ b/VIZDOOM/deadly_corridor.cfg @@ -0,0 +1,43 @@ + +# Lines starting with # are treated as comments (or with whitespaces+#). +# It doesn't matter if you use capital letters or not. +# It doesn't matter if you use underscore or camel notation for keys, e.g. episode_timeout is the same as episodeTimeout. + +doom_scenario_path = deadly_corridor.wad + +# Skill 5 is reccomanded for the scenario to be a challenge. +doom_skill = 5 + +# Rewards +death_penalty = 100 +#living_reward = 0 + +# Rendering options +screen_resolution = RES_320X240 +screen_format = CRCGCB +render_hud = true +render_crosshair = false +render_weapon = true +render_decals = false +render_particles = false +window_visible = true + +episode_timeout = 2100 + +# Available buttons +available_buttons = + { + MOVE_LEFT + MOVE_RIGHT + ATTACK + MOVE_FORWARD + MOVE_BACKWARD + TURN_LEFT + TURN_RIGHT + } + +# Game variables that will be in the state +available_game_variables = { HEALTH } + +mode = PLAYER + diff --git a/VIZDOOM/deadly_corridor.wad b/VIZDOOM/deadly_corridor.wad new file mode 100644 index 0000000000000000000000000000000000000000..29bf26068b2817b5392d94ef6dbf5748d9630414 GIT binary patch literal 10048 zcmeHNOK%(36}~iOw_z){O`A4Ot!ApEfo#jp3`tRPSp$(JMiJsjf~eL;H`16KNdu2G z0y%os2%tq5T@+3G54_W|Su}euvM%})gs!>_&>z5l-<=uGgS^Z|5;$#!o1V-QOuaEuCP{yknYXy>yy1 zgVO_2TlJFL4zJxOuIE{32c1KwUXtB%wp+<|tJ&^M(uHP^;zLpGy?(Rh`v?7wR8h-z zXS3b8ez)b>-ELx@>?Y>PZepJ7Cg#a*VxH_KdSy4!E4zu_4@(&&t%_Avfmt@Kxq8Wt ze7EL+xCFOcj^Iqp{|4PKHS*}zQkhz-hCoNDdN+Z#T%L*Z78t+y#FZ(M1XFgExj4$j 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zcY;m%iAW7YJD&g8_6$;+zw9{2cBdT?+MPZBu(Qc$vhe#8eEk5bXgDOr9KM~%G6eDG zC$!@2n?{qL_!!|LK4WwbcXk{Y)xP=mc&Xd!xCen@8LMlJmGRQk@sfdK+wV+KlWW|I zjjg-CGUE2F*sHpG>(-5sCE_G^(+TGJNW9kZ5Adtp>6ANd%&)Dlu5C6C9H%{Pm^WZA zDS4==-*ap)*qt`48}*RXFz685ABGq3Zt!y_$Zff{Hx(fR|DkcAx!E{zT8Du%?;YEx z-D#sry&>m#2_pLLmyO2CMq|D2e*$e}=zx)C?9!sWXYV-g+H`o@zz;~VZwsDdcb4~$ zoPErkPQ}AZ?vay>PKFseDV^;5^w6;n{QaqTQo$N!HF#?=#qqA|IaAhPNZO8PpJF0~ zd*8>UC|r-G%p12dxm7pPU`LSkL90$3`EDD!o|az9u*qCEnU#{gapDG>D}HAW0g~uQ zg p|H~Uf+{1ixVSRaRqbdIWR#>i!%kvLbmww&gcXB)$D~l8-{sS;{25 Date: Fri, 10 Apr 2020 01:57:13 +0200 Subject: [PATCH 22/63] cleanup --- a2c_cart.py | 186 --------------- a2c_cart_comb.py | 187 --------------- a2c_sync_cart.py | 209 ----------------- a2c_sync_cart_comb.py | 214 ----------------- a3c_cart.py | 219 ----------------- a3c_cart_comb.py | 221 ------------------ continuous_A3C.py | 133 ----------- .../A2C_Cart/A2C_cart__shared_train.png | Bin 52833 -> 0 bytes .../A2C_Cart/A2C_cart_shared_test.png | Bin 54786 -> 0 bytes .../A2C_Cart/A2C_cart_test.png | Bin 38018 -> 0 bytes .../A2C_Cart/A2C_cart_train.png | Bin 54174 -> 0 bytes .../A2C_cart_sync_shared_test.png | Bin 39810 -> 0 bytes .../A2C_cart_sync_shared_train.png | Bin 41995 -> 0 bytes .../A2C_Sync_Cart/A2C_cart_sync_test.png | Bin 41116 -> 0 bytes .../A2C_Sync_Cart/A2C_cart_sync_train.png | Bin 56798 -> 0 bytes .../A2C_Sync_Cart/A2C_cart_sync_train2.png | Bin 38975 -> 0 bytes .../A3C_Cart/A3C_cart_shared.png | Bin 56307 -> 0 bytes .../A3C_Cart/A3C_cart_shared_test.png | Bin 41649 -> 0 bytes .../A3C_Cart/A3C_cart_test.png | Bin 35292 -> 0 bytes .../A3C_Cart/A3C_cart_train.png | Bin 47516 -> 0 bytes results/cartpole.png | Bin 66098 -> 0 bytes save_model/a2c_cart_comb.pt | Bin 21893 -> 0 bytes save_model/a2c_sync_cart.pt | Bin 12951 -> 0 bytes save_model/a2c_sync_cart_comb.pt | Bin 26862 -> 0 bytes save_model/a3c_cart.pt | Bin 12954 -> 0 bytes save_model/a3c_cart_comb.pt | Bin 17168 -> 0 bytes shared_adam.py | 23 -- utils.py | 147 ------------ viz_utils.py | 151 ------------ 29 files changed, 1690 deletions(-) delete mode 100644 a2c_cart.py delete mode 100644 a2c_cart_comb.py delete mode 100644 a2c_sync_cart.py delete mode 100644 a2c_sync_cart_comb.py delete mode 100644 a3c_cart.py delete mode 100644 a3c_cart_comb.py delete mode 100644 continuous_A3C.py delete mode 100644 results/Presentable_Results/A2C_Cart/A2C_cart__shared_train.png delete mode 100644 results/Presentable_Results/A2C_Cart/A2C_cart_shared_test.png delete mode 100644 results/Presentable_Results/A2C_Cart/A2C_cart_test.png delete mode 100644 results/Presentable_Results/A2C_Cart/A2C_cart_train.png delete mode 100644 results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_test.png delete mode 100644 results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_train.png delete mode 100644 results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_test.png delete mode 100644 results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train.png delete mode 100644 results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train2.png delete mode 100644 results/Presentable_Results/A3C_Cart/A3C_cart_shared.png delete mode 100644 results/Presentable_Results/A3C_Cart/A3C_cart_shared_test.png delete mode 100644 results/Presentable_Results/A3C_Cart/A3C_cart_test.png delete mode 100644 results/Presentable_Results/A3C_Cart/A3C_cart_train.png delete mode 100644 results/cartpole.png delete mode 100644 save_model/a2c_cart_comb.pt delete mode 100644 save_model/a2c_sync_cart.pt delete mode 100644 save_model/a2c_sync_cart_comb.pt delete mode 100644 save_model/a3c_cart.pt delete mode 100644 save_model/a3c_cart_comb.pt delete mode 100644 shared_adam.py delete mode 100644 utils.py delete mode 100644 viz_utils.py diff --git a/a2c_cart.py b/a2c_cart.py deleted file mode 100644 index 3eae5c7..0000000 --- a/a2c_cart.py +++ /dev/null @@ -1,186 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -from shared_adam import SharedAdam -import numpy as np -import gym -from datetime import datetime -import time -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - -GAMMA = 0.9 -MAX_EP = 3000 - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi2 = nn.Linear(24, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v1 = nn.Linear(s_dim, 24) - self.v2 = nn.Linear(24, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) - logits = self.pi3(pi2) - v1 = F.relu(self.v1(x)) - v2 = F.relu(self.v2(v1)) - values = self.v3(v2) - return logits, values - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -if __name__ == "__main__": - # load global network - print ("Starting A2C Agent for Cartpole-v0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - actions = [] - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - - # Global variables for episodes - durations = [] - scores = [] - global_ep, global_ep_r = 1, 0. - name = 'w00' - total_step = 1 - stop_processes = False - - while global_ep < MAX_EP and stop_processes is False: - s = env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - # Initialize stopwatch for average episode duration - start = time.time() - if name == 'w00' and handleArguments().demo_mode: - env.render() - a = gnet.choose_action(v_wrap(s[None, :])) - actions.append(a) - s_, r, done, _ = env.step(a) - - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 600: # update network - # sync - optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - global_ep += 1 - - if global_ep_r == 0.: - global_ep_r = ep_r - else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - - end = time.time() - duration = end - start - durations.append(duration) - - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) - scores.append(int(global_ep_r)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: - stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: - stop_processes = True - break - - s = s_ - total_step += 1 - - if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: - print("Save model") - torch.save(gnet, "./save_model/a2c_cart.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) - - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - - # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, scores) - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 8 - } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Cartpole", fontsize = 16) - plt.show() - - sys.exit() diff --git a/a2c_cart_comb.py b/a2c_cart_comb.py deleted file mode 100644 index 23ece2f..0000000 --- a/a2c_cart_comb.py +++ /dev/null @@ -1,187 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -from shared_adam import SharedAdam -import numpy as np -import gym -from datetime import datetime -import time -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - -GAMMA = 0.9 -MAX_EP = 3000 - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi12 = nn.Linear(24, 48) - self.pi2 = nn.Linear(48, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v2 = nn.Linear(48, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi12 = F.relu(self.pi12(pi1)) - pi2 = F.relu(self.pi2(pi12)) - logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi12)) - values = self.v3(v2) - return logits, values - - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -if __name__ == "__main__": - # load global network - print ("Starting A2C Agent for Cartpole-v0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - actions = [] - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_cart_comb.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - - # Global variables for episodes - durations = [] - scores = [] - global_ep, global_ep_r = 1, 0. - name = 'w00' - total_step = 1 - stop_processes = False - - while global_ep < MAX_EP and stop_processes is False: - s = env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - # Initialize stopwatch for average episode duration - start = time.time() - if name == 'w00' and handleArguments().demo_mode: - env.render() - a = gnet.choose_action(v_wrap(s[None, :])) - actions.append(a) - s_, r, done, _ = env.step(a) - - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 600: # update network - # sync - optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - global_ep += 1 - - if global_ep_r == 0.: - global_ep_r = ep_r - else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - - end = time.time() - duration = end - start - durations.append(duration) - - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) - scores.append(int(global_ep_r)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: - stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: - stop_processes = True - break - - s = s_ - total_step += 1 - - if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: - print("Save model") - torch.save(gnet, "./save_model/a2c_cart_comb.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) - - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - - # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, scores) - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 8 - } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize = 16) - plt.show() - - sys.exit() diff --git a/a2c_sync_cart.py b/a2c_sync_cart.py deleted file mode 100644 index a577e69..0000000 --- a/a2c_sync_cart.py +++ /dev/null @@ -1,209 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -import torch.multiprocessing as mp -from shared_adam import SharedAdam -import numpy as np -import gym -import time -from datetime import datetime -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - - -UPDATE_GLOBAL_ITER = 50 -GAMMA = 0.9 -MAX_EP = 3000 - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi2 = nn.Linear(24, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v1 = nn.Linear(s_dim, 24) - self.v2 = nn.Linear(24, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) - logits = self.pi3(pi2) - v1 = F.relu(self.v1(x)) - v2 = F.relu(self.v2(v1)) - values = self.v3(v2) - return logits, values - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): - super(Worker, self).__init__() - self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - - def run(self): - total_step = 1 - stop_processes = False - scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - start = time.time() - if self.name == 'w00' and handleArguments().demo_mode: - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 600: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - time.sleep(0.2) - end = time.time() - time_done = end - start - 0.2 - - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 500 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 500 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break - - s = s_ - total_step += 1 - self.res_queue.put(None) - self.time_queue.put(None) - self.action_queue.put(None) - - -if __name__ == "__main__": - # load global network - print("Starting Synchronous A2C Agent for Cartpole-v0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_sync_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - - # record episode-reward and episode-duration to plot - res = [] - durations = [] - actions = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - actions.append(a) - else: - break - - [w.join() for w in workers] - - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300: - print("Save model") - torch.save(gnet, "./save_model/a2c_sync_cart.pt") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - confidence_intervall(actions) - - # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) - - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 10, - } - plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole") - plt.show() - - sys.exit() - diff --git a/a2c_sync_cart_comb.py b/a2c_sync_cart_comb.py deleted file mode 100644 index 68d259d..0000000 --- a/a2c_sync_cart_comb.py +++ /dev/null @@ -1,214 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -import torch.multiprocessing as mp -from shared_adam import SharedAdam -import numpy as np -import gym -import time -from datetime import datetime -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - -GAMMA = 0.9 -MAX_EP = 3000 - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) - self.pi2 = nn.Linear(48, 48) - self.pi3 = nn.Linear(48, a_dim) - self.v2 = nn.Linear(48, 48) - self.v3 = nn.Linear(48, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) - logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi1)) - values = self.v3(v2) - return logits, values - - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): - super(Worker, self).__init__() - self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - - def run(self): - total_step = 1 - stop_processes = False - scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - start = time.time() - if self.name == 'w00' and handleArguments().demo_mode: - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 600: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - - end = time.time() - - if not handleArguments().load_model: - time.sleep(0.5) - time_done = end - start - - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break - - s = s_ - total_step += 1 - self.res_queue.put(None) - self.time_queue.put(None) - self.action_queue.put(None) - - -if __name__ == "__main__": - # load global network - print("Starting Synchronous A2C Agent for Cartpole-v0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_sync_cart_comb.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - - # record episode-reward and episode-duration to plot - res = [] - durations = [] - actions = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - actions.append(a) - else: - break - - [w.join() for w in workers] - - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: - print("Save model") - torch.save(gnet, "./save_model/a2c_sync_cart_comb.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - - # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) - - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 8, - } - plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize = 16) - plt.show() - - sys.exit() - diff --git a/a3c_cart.py b/a3c_cart.py deleted file mode 100644 index ec59c1d..0000000 --- a/a3c_cart.py +++ /dev/null @@ -1,219 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -import torch.multiprocessing as mp -from shared_adam import SharedAdam -import numpy as np -import gym -import time -from datetime import datetime -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - -#UPDATE_GLOBAL_ITER = 20 -GAMMA = 0.9 -MAX_EP = 3000 - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi2 = nn.Linear(24, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v1 = nn.Linear(s_dim, 24) - self.v2 = nn.Linear(24, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) - logits = self.pi3(pi2) - v1 = F.relu(self.v1(x)) - v2 = F.relu(self.v2(v1)) - values = self.v3(v2) - return logits, values - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): - super(Worker, self).__init__() - self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - self.time_queue, self.action_queue = time_queue, action_queue - - def run(self): - total_step = 1 - stop_processes = False - scores = [] - - while self.g_ep.value < MAX_EP and stop_processes is False: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - start = time.time() - if self.name == 'w00' and handleArguments().demo_mode: - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - if done or ep_r == 600: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - end = time.time() - time_done = end - start - - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, - self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break - - s = s_ - total_step += 1 - self.time_queue.put(None) - self.res_queue.put(None) - self.action_queue.put(None) - -if __name__ == "__main__": - # load global network - print("STARTING A3C AGENT FOR CARTPOLE-V0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - # share the global parameters in multiprocessing - gnet.share_memory() - - # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) - - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - - # record episode-reward and duration-episode to plot - res = [] - durations = [] - actions = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - actions.append(a) - else: - break - - [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: - print("Save model") - torch.save(gnet, "./save_model/a3c_cart.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") - - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - - # Plot results after each run - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) - - # Finish plots - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 8, - } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Cartpole") - plt.show() - - sys.exit() - diff --git a/a3c_cart_comb.py b/a3c_cart_comb.py deleted file mode 100644 index bc4b1b8..0000000 --- a/a3c_cart_comb.py +++ /dev/null @@ -1,221 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall -import matplotlib.pyplot as plt -import torch.nn.functional as F -import torch.multiprocessing as mp -from shared_adam import SharedAdam -import numpy as np -import gym -import time -from datetime import datetime -import sys -import os -os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 20 -GAMMA = 0.9 -MAX_EP = 3000 - - - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) - self.pi2 = nn.Linear(48, 48) - self.pi3 = nn.Linear(48, a_dim) - self.v2 = nn.Linear(48, 48) - self.v3 = nn.Linear(48, 1) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) - self.distribution = torch.distributions.Categorical - - def forward(self, x): - pi1 = F.relu(self.pi1(x)) - pi2 = F.relu(self.pi2(pi1)) - logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi1)) - values = self.v3(v2) - return logits, values - - def set_init(layers): - for layer in layers: - nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) - nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) - - def choose_action(self, s): - self.eval() - logits, _ = self.forward(s) - prob = F.softmax(logits, dim=1).data - m = self.distribution(prob) - return m.sample().numpy()[0] - - def loss_func(self, s, a, v_t): - self.train() - logits, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - probs = F.softmax(logits, dim=1) - m = self.distribution(probs) - exp_v = m.log_prob(a) * td.detach().squeeze() - a_loss = -exp_v - total_loss = (c_loss + a_loss).mean() - return total_loss - - -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): - super(Worker, self).__init__() - self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - self.time_queue, self.action_queue = time_queue, action_queue - - def run(self): - total_step = 1 - stop_processes = False - scores = [] - - while self.g_ep.value < MAX_EP and stop_processes is False: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - start = time.time() - if self.name == 'w00' and handleArguments().demo_mode: - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a) - if done: r = -1 - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append(r) - - - if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - if done or ep_r == 600: # done and print information - end = time.time() - time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break - s = s_ - total_step += 1 - self.time_queue.put(None) - self.res_queue.put(None) - self.action_queue.put(None) - - -if __name__ == "__main__": - # load global network - print("STARTING A3C AGENT FOR CARTPOLE-V0") - time.sleep(3) - timedelta_sum = datetime.now() - timedelta_sum -= timedelta_sum - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - - # Reinitialize Agent to make 5 trials - for i in range(3): - starttime = datetime.now() - if handleArguments().load_model: - gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart_comb.pt") - gnet.eval() - else: - gnet = Net(N_S, N_A) - - # share the global parameters in multiprocessing - gnet.share_memory() - - # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) - - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - - # parallel training - if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - - # record episode-reward and duration-episode to plot - res = [] - durations = [] - actions = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - actions.append(a) - else: - break - - [w.join() for w in workers] - - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: - print("Save model") - torch.save(gnet, "./save_model/a3c_cart_comb.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") - - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 - - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - - # Plot results after each run - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) - - # Finish plots - font = {'family': 'serif', - 'color': 'darkred', - 'weight': 'normal', - 'size': 8, - } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Cartpole (shared NN)") - plt.show() - - sys.exit() - diff --git a/continuous_A3C.py b/continuous_A3C.py deleted file mode 100644 index 35f0cb6..0000000 --- a/continuous_A3C.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -Reinforcement Learning (A3C) using Pytroch + multiprocessing. -The most simple implementation for continuous action. - -View more on my Chinese tutorial page [莫烦Python](https://morvanzhou.github.io/). -""" - -import torch -import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record -import torch.nn.functional as F -import torch.multiprocessing as mp -from shared_adam import SharedAdam -import gym -import math, os -os.environ["OMP_NUM_THREADS"] = "1" - -UPDATE_GLOBAL_ITER = 5 -GAMMA = 0.9 -MAX_EP = 3000 -MAX_EP_STEP = 200 - -env = gym.make('Pendulum-v0') -N_S = env.observation_space.shape[0] -N_A = env.action_space.shape[0] - - -class Net(nn.Module): - def __init__(self, s_dim, a_dim): - super(Net, self).__init__() - self.s_dim = s_dim - self.a_dim = a_dim - self.a1 = nn.Linear(s_dim, 200) - self.mu = nn.Linear(200, a_dim) - self.sigma = nn.Linear(200, a_dim) - self.c1 = nn.Linear(s_dim, 100) - self.v = nn.Linear(100, 1) - set_init([self.a1, self.mu, self.sigma, self.c1, self.v]) - self.distribution = torch.distributions.Normal - - def forward(self, x): - a1 = F.relu6(self.a1(x)) - mu = 2 * F.tanh(self.mu(a1)) - sigma = F.softplus(self.sigma(a1)) + 0.001 # avoid 0 - c1 = F.relu6(self.c1(x)) - values = self.v(c1) - return mu, sigma, values - - def choose_action(self, s): - self.training = False - mu, sigma, _ = self.forward(s) - m = self.distribution(mu.view(1, ).data, sigma.view(1, ).data) - return m.sample().numpy() - - def loss_func(self, s, a, v_t): - self.train() - mu, sigma, values = self.forward(s) - td = v_t - values - c_loss = td.pow(2) - - m = self.distribution(mu, sigma) - log_prob = m.log_prob(a) - entropy = 0.5 + 0.5 * math.log(2 * math.pi) + torch.log(m.scale) # exploration - exp_v = log_prob * td.detach() + 0.005 * entropy - a_loss = -exp_v - total_loss = (a_loss + c_loss).mean() - return total_loss - - -class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, name): - super(Worker, self).__init__() - self.name = 'w%i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue - self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('Pendulum-v0').unwrapped - - def run(self): - total_step = 1 - while self.g_ep.value < MAX_EP: - s = self.env.reset() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - for t in range(MAX_EP_STEP): - if self.name == 'w0': - self.env.render() - a = self.lnet.choose_action(v_wrap(s[None, :])) - s_, r, done, _ = self.env.step(a.clip(-2, 2)) - if t == MAX_EP_STEP - 1: - done = True - ep_r += r - buffer_a.append(a) - buffer_s.append(s) - buffer_r.append((r+8.1)/8.1) # normalize - - if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net - # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - buffer_s, buffer_a, buffer_r = [], [], [] - - if done: # done and print information - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.name) - break - s = s_ - total_step += 1 - - self.res_queue.put(None) - - -if __name__ == "__main__": - gnet = Net(N_S, N_A) # global network - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=1e-4, betas=(0.95, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue() - - # parallel training - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, i) for i in range(mp.cpu_count())] - [w.start() for w in workers] - res = [] # record episode reward to plot - while True: - r = res_queue.get() - if r is not None: - res.append(r) - else: - break - [w.join() for w in workers] - - import matplotlib.pyplot as plt - plt.plot(res) - plt.ylabel('Moving average ep reward') - plt.xlabel('Step') - plt.show() diff --git a/results/Presentable_Results/A2C_Cart/A2C_cart__shared_train.png b/results/Presentable_Results/A2C_Cart/A2C_cart__shared_train.png deleted file mode 100644 index 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zrJotv0$MUm$o{?;pY65?EaFzvzvf!Qp3F=-Ni&VE94w~aza38tyxOs=at6a8|9pCJ z(m?Khx*aRZD1zIK#&Do4oVoiU3$AFU!Ox8euvFkoZlDx;WB+3SKbKZo4m?O-_5MVD zuTyt>IFTGMqL1!h%_MvFp?@q;qhHQ$#wlA* Date: Fri, 10 Apr 2020 14:41:49 +0200 Subject: [PATCH 23/63] VIZDOOM - create A3C model --- CARTPOLE/shared_adam.py | 0 VIZDOOM/shared_adam.py | 0 VIZDOOM/vizdoom_a2c.py | 68 ++++++++--------------------------------- VIZDOOM/vizdoom_a3c.py | 0 4 files changed, 12 insertions(+), 56 deletions(-) create mode 100644 CARTPOLE/shared_adam.py create mode 100644 VIZDOOM/shared_adam.py create mode 100644 VIZDOOM/vizdoom_a3c.py diff --git a/CARTPOLE/shared_adam.py b/CARTPOLE/shared_adam.py new file mode 100644 index 0000000..e69de29 diff --git a/VIZDOOM/shared_adam.py b/VIZDOOM/shared_adam.py new file mode 100644 index 0000000..e69de29 diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index ad4bd09..9cd8f83 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -1,30 +1,25 @@ -from absl import app, flags + import itertools -from random import sample, randint, random import numpy as np from skimage.transform import resize -from time import time, sleep +import time import torch from torch import nn import torch.nn.functional as F from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall from shared_adam import SharedAdam -import gym - -env = gym.make('CartPole-v0').unwrapped -N_S = env.observation_space.shape[0] -N_A = env.action_space.n +import os +os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 MAX_EP = 30 -FLAGS = flags.FLAGS frame_repeat = 12 resolution = (30, 45) -default_config_file_path = "//VIZDOOM/deadly_corridor.cfg" +config_file_path = "deadly_corridor.cfg" def initialize_vizdoom(config): @@ -45,9 +40,9 @@ def game_state(game): return preprocess(game.get_state().screen_buffer) -class QNet(nn.Module): +class Net(nn.Module): def __init__(self, a_dim): - super(QNet, self).__init__() + super(Net, self).__init__() self.s_dim = 45 self.a_dim = a_dim self.pi1 = nn.Linear(self.s_dim, 120) @@ -103,7 +98,7 @@ def loss_func(self, s, a, v_t): for j in range(len(logits)): logits[countrow][i] += logits[j][i] if j % 30 == 0: - new_logits[countrow][i] = logits[countrow][i] + new_logits[countrow][i] = logits[countrow][i] countrow += 1 probs = F.softmax(new_logits, dim=1) m = self.distribution(probs) @@ -113,28 +108,10 @@ def loss_func(self, s, a, v_t): return total_loss - def train_step(self, s1, target_q): - output = self(s1) - loss = self.criterion(output, target_q) - self.optimizer.zero_grad() - loss.backward() - self.optimizer.step() - return loss - - def learn_from_memory(self): - if self.memory.size < FLAGS.batch_size: return - s1, a, s2, isterminal, r = self.memory.get_sample(FLAGS.batch_size) - q = self(s2).detach() - q2, _ = torch.max(q, dim=1) - target_q = self(s1).detach() - idxs = (torch.arange(target_q.shape[0]), a) - target_q[idxs] = r + FLAGS.discount * (1-isterminal) * q2 - self.train_step(s1, target_q) - -def main(_): +if __name__ == '__main__': - game = initialize_vizdoom(FLAGS.config) + game = initialize_vizdoom(config_file_path) statesize = (game_state(game).shape[0]) state=game_state(game) print("Current State:" , state, "\n") @@ -145,11 +122,10 @@ def main(_): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) - model = QNet(len(actions)) + model = Net(len(actions)) opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r = 1, 0. - name = 'w00' total_step = 1 while global_ep < MAX_EP: @@ -161,17 +137,14 @@ def main(_): done = False a = model.choose_action(state) actions.append(a) - #s_, r, done, _ = env.step(a) r = game.make_action(actions[a], frame_repeat) - #print("Reward:", r) if game.is_episode_finished(): done = True else: s_ = game_state(game) - #if done: r = -1 ep_r += r buffer_a.append(a) buffer_s.append(state) @@ -198,21 +171,4 @@ def main(_): game.close() -if __name__ == '__main__': - flags.DEFINE_integer('batch_size', 64, 'Batch size') - flags.DEFINE_float('learning_rate', 0.00025, 'Learning rate') - flags.DEFINE_float('discount', 0.99, 'Discount factor') - flags.DEFINE_integer('replay_memory', 10000, 'Replay memory capacity') - flags.DEFINE_integer('epochs', 20, 'Number of epochs') - flags.DEFINE_integer('iters', 2000, 'Iterations per epoch') - flags.DEFINE_integer('watch_episodes', 10, 'Trained episodes to watch') - flags.DEFINE_integer('test_episodes', 100, 'Episodes to test with') - flags.DEFINE_string('config', default_config_file_path, - 'Path to the config file') - flags.DEFINE_boolean('skip_training', False, 'Set to skip training') - flags.DEFINE_boolean('load_model', False, 'Load the model from disk') - flags.DEFINE_string('save_path', 'model-doom.pth', - 'Path to save/load the model') - app.run(main) - diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py new file mode 100644 index 0000000..e69de29 From d3a40336d2fdfad01f6013042e073ac66b1a8b51 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 10 Apr 2020 14:48:23 +0200 Subject: [PATCH 24/63] VIZDOOM - create A3C part2 --- VIZDOOM/vizdoom_a3c.py | 223 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 223 insertions(+) diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index e69de29..6444808 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -0,0 +1,223 @@ +import itertools +import numpy as np +from skimage.transform import resize +import time +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, push_and_pull, record, plotter_ep_time, confidence_intervall +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + + +GAMMA = 0.9 +MAX_EP = 30 +frame_repeat = 12 +resolution = (30, 45) +config_file_path = "deadly_corridor.cfg" + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + game.set_window_visible(False) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + + +class Net(nn.Module): + def __init__(self, a_dim): + super(Net, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 120) + self.pi2 = nn.Linear(120, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v1 = nn.Linear(self.s_dim, 120) + self.v2 = nn.Linear(120, 360) + self.v3 = nn.Linear(360, 1) + + #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) + + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(len(actions)) # local network + self.game = initialize_vizdoom(config_file_path) + self.time_queue, self.action_queue = time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + self.game.new_episode() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + done = False + a = self.lnet.choose_action(state) + + r = self.game.make_action(actions[a], frame_repeat) + + if self.game.is_episode_finished(): + done = True + else: + s_ = game_state(self.game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + game.get_total_reward() + buffer_s, buffer_a, buffer_r = [], [], [] + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == '__main__': + print("Current State:", state, "\n") + print("Statesize:", statesize, "\n") + + print("Action Size: ", n) + print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + + model = Net(len(actions)) + + opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in + range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + action.append(a) + else: + break + + [w.join() for w in workers] + + game.close() + sys.exit() + From 06a759fc8dc683eea3ed0fe1191ef27c40d712be Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 10 Apr 2020 15:14:22 +0200 Subject: [PATCH 25/63] VIZDOOM - create synchronous a2c --- VIZDOOM/shared_adam.py | 22 ++++ VIZDOOM/vizdoom_a2c_sync.py | 224 ++++++++++++++++++++++++++++++++++++ 2 files changed, 246 insertions(+) create mode 100644 VIZDOOM/vizdoom_a2c_sync.py diff --git a/VIZDOOM/shared_adam.py b/VIZDOOM/shared_adam.py index e69de29..082fa69 100644 --- a/VIZDOOM/shared_adam.py +++ b/VIZDOOM/shared_adam.py @@ -0,0 +1,22 @@ +""" +Shared optimizer, the parameters in the optimizer will shared in the multiprocessors. +""" + +import torch + + +class SharedAdam(torch.optim.Adam): + def __init__(self, params, lr=1e-3, betas=(0.9, 0.99), eps=1e-8, + weight_decay=0): + super(SharedAdam, self).__init__(params, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + # State initialization + for group in self.param_groups: + for p in group['params']: + state = self.state[p] + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p.data) + state['exp_avg_sq'] = torch.zeros_like(p.data) + + # share in memory + state['exp_avg'].share_memory_() + state['exp_avg_sq'].share_memory_() \ No newline at end of file diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py new file mode 100644 index 0000000..416ae99 --- /dev/null +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -0,0 +1,224 @@ +import itertools +import numpy as np +from skimage.transform import resize +import time +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, push_and_pull, record, plotter_ep_time, confidence_intervall +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + + +GAMMA = 0.9 +MAX_EP = 30 +frame_repeat = 12 +resolution = (30, 45) +config_file_path = "deadly_corridor.cfg" + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + game.set_window_visible(False) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + + +class Net(nn.Module): + def __init__(self, a_dim): + super(Net, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 120) + self.pi2 = nn.Linear(120, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v1 = nn.Linear(self.s_dim, 120) + self.v2 = nn.Linear(120, 360) + self.v3 = nn.Linear(360, 1) + + #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) + + set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v1 = F.relu(self.v1(x)) + v2 = F.relu(self.v2(v1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.gnet, self.opt = gnet, opt + self.lnet = Net(len(actions)) # local network + self.game = initialize_vizdoom(config_file_path) + self.time_queue, self.action_queue = time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + self.game.new_episode() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + done = False + a = self.lnet.choose_action(state) + + r = self.game.make_action(actions[a], frame_repeat) + + if self.game.is_episode_finished(): + done = True + else: + s_ = game_state(self.game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or ep_r == 600: # update network + # sync + push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + time.sleep(10) + game.get_total_reward() + buffer_s, buffer_a, buffer_r = [], [], [] + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break + + s = s_ + total_step += 1 + + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == '__main__': + print("Current State:", state, "\n") + print("Statesize:", statesize, "\n") + + print("Action Size: ", n) + print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + + model = Net(len(actions)) + + opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + + # parallel training + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in + range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + action.append(a) + else: + break + + [w.join() for w in workers] + + game.close() + sys.exit() + From 608e20af28955124c20a98c69ca6107211249363 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 10 Apr 2020 16:54:44 +0200 Subject: [PATCH 26/63] VIZDOOM - add plotter and game replay in a2c --- VIZDOOM/viz_utils.py | 7 +- VIZDOOM/vizdoom_a2c.py | 192 ++++++++++++++++++++++++++++------------- 2 files changed, 138 insertions(+), 61 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 923f98b..e7050a1 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -92,18 +92,19 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, ac def plotter_ep_rew(ax2, scores): ax2.plot(scores) - ax2.axhline(y=200.00, color='r') - ax2.set_ylim(0,700) + #ax2.axhline(y=200.00, color='r') + ax2.set_ylim(-150,300) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.04) + ax1.set_ylim(0,10) ax1.set_ylabel('Duration of Episode') +# TODO: check for reasonable count length and adjust def confidence_intervall(actions, load_model = False): count = 1 probab_count = 0 diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 9cd8f83..3a903e2 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -1,8 +1,9 @@ - import itertools import numpy as np from skimage.transform import resize import time +from datetime import datetime +import matplotlib.pyplot as plt import torch from torch import nn import torch.nn.functional as F @@ -25,7 +26,10 @@ def initialize_vizdoom(config): game = DoomGame() game.load_config(config) - game.set_window_visible(True) + if handleArguments().demo_mode: + game.set_window_visible(True) + else: + game.set_window_visible(False) game.set_mode(Mode.PLAYER) game.set_screen_format(ScreenFormat.GRAY8) game.set_screen_resolution(ScreenResolution.RES_640X480) @@ -40,6 +44,18 @@ def game_state(game): return preprocess(game.get_state().screen_buffer) +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + +print("Current State:" , state, "\n") +print("Statesize:" , statesize, "\n") +print ("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -111,63 +127,123 @@ def loss_func(self, s, a, v_t): if __name__ == '__main__': - game = initialize_vizdoom(config_file_path) - statesize = (game_state(game).shape[0]) - state=game_state(game) - print("Current State:" , state, "\n") - print("Statesize:" , statesize, "\n") - n = game.get_available_buttons_size() - print ("Action Size: ", n) - actions = [list(a) for a in itertools.product([0, 1], repeat=n)] - print("All possible Actions:", actions, "\n", "Total: ", len(actions)) - - - model = Net(len(actions)) - opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - - global_ep, global_ep_r = 1, 0. - total_step = 1 - - while global_ep < MAX_EP: - game.new_episode() - buffer_s, buffer_a, buffer_r = [], [], [] - ep_r = 0. - while True: - - done = False - a = model.choose_action(state) - actions.append(a) - - r = game.make_action(actions[a], frame_repeat) - - if game.is_episode_finished(): - done = True - else: - s_ = game_state(game) - - ep_r += r - buffer_a.append(a) - buffer_s.append(state) - buffer_r.append(r) - - if done or ep_r == 600: # update network - # sync - optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - game.get_total_reward() - buffer_s, buffer_a, buffer_r = [], [], [] - - global_ep += 1 - - if global_ep_r == 0.: - global_ep_r = ep_r + print ("Starting A2C Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) + + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + action = [] + + for i in range(3): + starttime = datetime.now() + + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./doom_save_model/a2c_doom.pt") + model.eval() + else: + model = Net(len(actions)) + + opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r = 1, 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP: + game.new_episode() + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + # Initialize stopwatch for average episode duration + start = time.time() + done = False + + a = model.choose_action(state) + action.append(a) + r = game.make_action(actions[a], frame_repeat) + + if game.is_episode_finished(): + done = True else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r) - break - - s = s_ - total_step += 1 + s_ = game_state(game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or ep_r == 50: # update network + # sync + optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + print("Acion_array:", action) + game.get_total_reward() + buffer_s, buffer_a, buffer_r = [], [], [] + + global_ep += 1 + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + + end = time.time() + duration = end - start + durations.append(duration) + + print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + scores.append(int(global_ep_r)) + + # TODO: check for reasonable reward and adjust + if handleArguments().load_model: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 10: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 10: + stop_processes = True + break + + s = s_ + total_step += 1 + + + # TODO: check for reasonable reward and adjust + if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + print("Save model") + torch.save(model, "./doom_save_model/a2c_doom.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + #if handleArguments().load_model: + # confidence_intervall(action, True) + #else: + # confidence_intervall(action) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Vizdoom", fontsize = 16) + plt.show() game.close() From 52ac70c53c4e50a4b94775adfe343b5d38a1473f Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 10 Apr 2020 18:22:43 +0200 Subject: [PATCH 27/63] VIZDOOM - plotter and stopwatch for a2c_sync and a3c --- VIZDOOM/viz_utils.py | 2 +- VIZDOOM/vizdoom_a2c.py | 8 +-- VIZDOOM/vizdoom_a2c_sync.py | 120 ++++++++++++++++++++++++++---------- VIZDOOM/vizdoom_a3c.py | 120 ++++++++++++++++++++++++++---------- 4 files changed, 177 insertions(+), 73 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index e7050a1..59c2e7a 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -100,7 +100,7 @@ def plotter_ep_rew(ax2, scores): def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,10) + ax1.set_ylim(0,20) ax1.set_ylabel('Duration of Episode') diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 3a903e2..731e0f5 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -17,7 +17,7 @@ GAMMA = 0.9 -MAX_EP = 30 +MAX_EP = 100 frame_repeat = 12 resolution = (30, 45) config_file_path = "deadly_corridor.cfg" @@ -146,7 +146,7 @@ def loss_func(self, s, a, v_t): else: model = Net(len(actions)) - opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer # Global variables for episodes durations = [] @@ -182,8 +182,8 @@ def loss_func(self, s, a, v_t): if done or ep_r == 50: # update network # sync optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - print("Acion_array:", action) - game.get_total_reward() + #print("Acion_array:", action) + print("Total Reward:", game.get_total_reward()) buffer_s, buffer_a, buffer_r = [], [], [] global_ep += 1 diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index 416ae99..6d55511 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -2,6 +2,8 @@ import numpy as np from skimage.transform import resize import time +from datetime import datetime +import matplotlib.pyplot as plt import torch from torch import nn import torch.nn.functional as F @@ -46,6 +48,11 @@ def game_state(game): n = game.get_available_buttons_size() actions = [list(a) for a in itertools.product([0, 1], repeat=n)] +print("Current State:", state, "\n") +print("Statesize:", statesize, "\n") + +print("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) class Net(nn.Module): def __init__(self, a_dim): @@ -157,18 +164,19 @@ def run(self): push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) time.sleep(10) game.get_total_reward() - buffer_s, buffer_a, buffer_r = [], [], [] + #buffer_s, buffer_a, buffer_r = [], [], [] end = time.time() time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) + + # TODO: check for reasonable reward and adjust if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: stop_processes = True else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: stop_processes = True break @@ -181,43 +189,87 @@ def run(self): if __name__ == '__main__': - print("Current State:", state, "\n") - print("Statesize:", statesize, "\n") - print("Action Size: ", n) - print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + print ("Starting A3C Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) - model = Net(len(actions)) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + action = [] - opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + for i in range (3): + starttime = datetime.now() - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./doom_save_model/a3c_doom.pt") + model.eval() + else: + model = Net(len(actions)) + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer - # parallel training - if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in - range(mp.cpu_count())] - [w.start() for w in workers] + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - # record episode-reward and duration-episode to plot - res = [] - durations = [] - action = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - action.append(a) + # parallel training + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] else: - break - - [w.join() for w in workers] + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in + range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + action.append(a) + else: + break + + [w.join() for w in workers] + + # TODO: check for reasonable reward and adjust + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./doom_save_model/a2c_doom.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + timedelta_sum += timedelta / 3 + + # Get results for confidence intervall + # if handleArguments().load_model: + # confidence_intervall(action, True) + # else: + # confidence_intervall(action) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Vizdoom", fontsize=16) + plt.show() game.close() sys.exit() diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 6444808..c77528b 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -2,6 +2,8 @@ import numpy as np from skimage.transform import resize import time +from datetime import datetime +import matplotlib.pyplot as plt import torch from torch import nn import torch.nn.functional as F @@ -46,6 +48,11 @@ def game_state(game): n = game.get_available_buttons_size() actions = [list(a) for a in itertools.product([0, 1], repeat=n)] +print("Current State:", state, "\n") +print("Statesize:", statesize, "\n") + +print("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) class Net(nn.Module): def __init__(self, a_dim): @@ -156,18 +163,19 @@ def run(self): # sync push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) game.get_total_reward() - buffer_s, buffer_a, buffer_r = [], [], [] + #buffer_s, buffer_a, buffer_r = [], [], [] end = time.time() time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) + + # TODO: check for reasonable reward and adjust if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: stop_processes = True else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: stop_processes = True break @@ -180,43 +188,87 @@ def run(self): if __name__ == '__main__': - print("Current State:", state, "\n") - print("Statesize:", statesize, "\n") - print("Action Size: ", n) - print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + print ("Starting A3C Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) - model = Net(len(actions)) + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + action = [] - opt = SharedAdam(model.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + for i in range (3): + starttime = datetime.now() - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./doom_save_model/a3c_doom.pt") + model.eval() + else: + model = Net(len(actions)) + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer - # parallel training - if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in - range(mp.cpu_count())] - [w.start() for w in workers] + global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - # record episode-reward and duration-episode to plot - res = [] - durations = [] - action = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - durations.append(t) - action.append(a) + # parallel training + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] else: - break - - [w.join() for w in workers] + workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in + range(mp.cpu_count())] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + durations.append(t) + action.append(a) + else: + break + + [w.join() for w in workers] + + # TODO: check for reasonable reward and adjust + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./doom_save_model/a2c_doom.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + timedelta_sum += timedelta / 3 + + # Get results for confidence intervall + # if handleArguments().load_model: + # confidence_intervall(action, True) + # else: + # confidence_intervall(action) + + # Plot results + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Vizdoom", fontsize=16) + plt.show() game.close() sys.exit() From e139bbe20601ce75b9e5c79f6f37a5fafdcd9892 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sat, 11 Apr 2020 23:45:33 +0200 Subject: [PATCH 28/63] CARTPOLE - refactor synchronous coordinator in A2C_Sync --- .../__pycache__/cart_utils.cpython-36.pyc | Bin 0 -> 4582 bytes .../__pycache__/shared_adam.cpython-36.pyc | Bin 0 -> 1013 bytes CARTPOLE/a2c_cart.py | 9 +++---- CARTPOLE/a2c_cart_comb.py | 6 ++--- CARTPOLE/a2c_sync_cart.py | 14 +++++------ CARTPOLE/a2c_sync_cart_comb.py | 7 +++--- CARTPOLE/a3c_cart.py | 8 +++---- CARTPOLE/a3c_cart_comb.py | 8 +++---- .../A2C_cart_sync_shared_train.png | Bin 41995 -> 40908 bytes CARTPOLE/cart_save_model/a2c_sync_cart.pt | Bin 12951 -> 12985 bytes CARTPOLE/cart_utils.py | 15 +++++++++--- CARTPOLE/shared_adam.py | 22 ++++++++++++++++++ 12 files changed, 60 insertions(+), 29 deletions(-) create mode 100644 CARTPOLE/__pycache__/cart_utils.cpython-36.pyc create mode 100644 CARTPOLE/__pycache__/shared_adam.cpython-36.pyc diff --git a/CARTPOLE/__pycache__/cart_utils.cpython-36.pyc b/CARTPOLE/__pycache__/cart_utils.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ea2137109642633af9797d91873ed5c06736e6cb GIT binary patch literal 4582 zcmb_g&2JmW72nw}E4EuB+Qh z*VAovQ0vw;*XTBMyVPA0-k`a~xXqojSZD2s(( zL_?9+_MwW=VaiOBJ3W&+c&Me{P5kl~K}us_l zUhHK;xnZtbr$}XQTh+E?It-H0Fx%^xT`$RkP|9#mk}dyY$O@W&{d{dZ9g4NVDC&vJ zmoBZ{+e>=eGELG^9_-}7jr_2`dRv4tiIV=>%^P<=-T3(S2W!1h7Qv{9;{0N^r`(<3 zkqomI?c4=1SPg%&3a-bfkB3yFIj!DXV0JH+#{0%^E=*Vf^#*3i1{Ti7ZCKogwqQFj*>>O0TT9jnjCiYz*wOK4mb-t@{Rg&eUM=l7U;WqB8JGha{IS$Lc{9 zBee&ne4^*-r8}i}YTSNoCXM;~7{e%yLpJuLcgT(T9<50ezSq#-XbzUB-F>BK;(M*B zQJO@jCU$x3*XE=(Iaam?$I4^D@rVDw-i*PrURV7ClOGl*rpBP%Fo@q>FKdI7#i_yR zsd38S)&mox=OA+h63;*)wA;X&56|5<68hGWaqR-$p$qJjdr2Z4rrmr0-*`Hv9-0w4 zw$2NxvX#n5q2$^tmCeH-h~)v=Ii(3c@s-X`9mFDPpqWvZ5iG8~G69Lv@(IJ5WcybzgcZHEbu#SPgX z4MkGqsurgq4~8ihv2u38cqEiL%;2usDCni5q);vW2DVa&fJePT)eia2ab=}hq3k#j zpnXkwnm|Y%D04H%FT_tOrymZ7VYhxHzE!m!r<({8?91K>u%NX$+S(E_*a?bI_92l; zh=x(RW}GRrpDhN-QZ~xB1E>^aqc}bbljj#f3}!ReykOd_%^Ix9Ja7$miJh}N=CO0& zJ#*PKG23J{Oa25adK7f@H1Io!sBh6JO&w9*K}1>qBceP+R232BE<{wVtd*70(-GyB zRYa6`5K-0VBFfeg<$PyEA=;jcC>??Gh^l@^MAZ)>sy>S-^qUkZi@q(}Q2MBEOYO?f z`F2a5q8BOK%F{$vh@2tv0uee5d5*~Uh)@xbRA1!xiTFf*K;#vWPD9eU$u%OEiCiIa zmB<@J-XuclTmFd1+eFrh{Fn%x*U{7omyCwdSiFmIUDJ*@g!#NfNP4eFiqJzX>7!HX z6EHB8F*SbQM#3{})C|t9p{xXqD#ItnUE^1j2g%8VZQTC)pV#}pZ4>+X^|AFqwmxo@{s&ng*Zr3-UfLShOaJ|m3<)xo@5<0ub7 z7sM%&P6q?Z%0`89GKt)y9EoheJ5_m&X6gr_8z4jlq^hbEdGK%~kU?sMH|Bg*^ExN6 zP&Go0QRZ`Yf>gD5dae|`RPtZqGdZ~;RrM8J**)_`gs!gfw=w<ad8uL7^Wn-xZW~ z{Y*$$HOi4eyOlWuOWIkS7Trp?yB$YJhq{{Y#nBMi6DHn^!%ZYy&F^X+U$Mg7E6UBG zyvP@g(j;+6>d2LW6puKR%xfTIoTfI4`~`Z?n1muuo8*UJpyYx{=0*VUIZ)lcqZORH z%~#jQC!TtUpKd+XvSZ1QAy94*`2=LP^~(UUb9cffwLKP^v z4pg8xJV?Y}q1FP{nB1ILNU!$p9gX5{O&r7mL%h9G0>xc3CKlq%LX=PpZh$8syMa0D zEX~<4!c1Mrp;@djA1>bqP~vuJYm)#VVeK~>^4c7ri4%xIyJEBf8_ohKaJOwt0DW{U z>bcTgF(@W^7122#Iju*gHZziM=hE0*8ruIV4RZ_9^vI#Kxh#M;y)(26vMShFP45G9 zW#yTj>5=DTd2?B47i4({vM^VA&&bjvhq83^)MvR!`_VqR>A1BaQRO$II4UBMult|w z6HXrAyA{d2@NcHcR>Vcp6aGgD92^iN_JNuFx8Ho(2WFm6uH#Z8{8{qwUzG%@U-hHh z?`@~KNc>GvJQ5<=Czl`FJ^;b^(|brG3IG3opySL6+)wcxUE7qMKa_=}FzZxRWwyaw zUO=Bx2Jln2k!k&BXsWw3>&>kpKf;UMDqRc$E_N;-vR4k-tBY~eKC}yN2y>%JNXjpP zP_3iC8la8`h~t3+{IP1L$IgShV4Y{LndjLl*49*hhBZA}NDpD!gZmQYQo0dQ27Qcc zpw6EbWl*}eK89!O5!_vIZ#^8r)grVD8@HzVc=baKICKBz?T>tbx*6=G6Y}$LN6=+e z4Eh&wh{`S|QzfNsEJ?NR-`p)Zo12sco|!-Q>-euF}e1k#$l zEr2`S*{O~jtE-1#8KUCxmD||POpr#t-kovh_NVK$R0{>KGVFsXd0RHAv z9KJzaiR#EX?#weIX(*oy>I2FXg=(%|C>K0JzKVnTBYrudR|9C*W6NwA2d-y2>{e$* zRf2%0y&#ZO>t}b>lltC%arSQlnf64UW1p(EFd$#U3T0Euo`F0b%R0Q4 zdcuo&#)Yf}-D2m@2ethmifVFlZF(5@qbx{8;&UkzAs5`QPy*WUHJ+I+Pn*07z)=fB zi3Telqg~8O%swi&%wt4Qi$Gznn%1?#ym6@|_SVeOaB{9bb# z^{#Ehn?P&!Mx7`l63W`)nJ1OWQ>iNZJZN_^%DC?hRH;y37Ck8{*)gO*gd0Lm(O6;8zk2BUriyy->I>;W%t8X4 zSn5p3m^Ui;Ja(~zXYs6A!Na-9@aiF1IP3nz5PH&t1yCnKkAS$MK@UeEY2DIVbz#0i zE0@wZVf{$^6-s-3FPfYb$<@rXH=jolrQ(w2&``%>Ui+6dX!*5BQYUsW!o?1xu_= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a2c_cart.pt") + torch.save(gnet, "./cart_save_model/a2c_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index 23ece2f..e26f82f 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F from shared_adam import SharedAdam @@ -90,7 +90,7 @@ def loss_func(self, s, a, v_t): if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_cart_comb.pt") + gnet = torch.load("./cart_save_model/a2c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -155,7 +155,7 @@ def loss_func(self, s, a, v_t): if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a2c_cart_comb.pt") + torch.save(gnet, "./cart_save_model/a2c_cart_comb.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index 900cdb7..5f295e1 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -1,12 +1,12 @@ """ -Reinforcement Learning (A3C) using Pytroch + multiprocessing. +Reinforcement Learning (A2C-Sync) using Pytroch + multiprocessing. The most simple implementation for continuous action. """ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -108,13 +108,13 @@ def run(self): if done or ep_r == 600: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - end = time.time() + time_done = end - start + + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) if not handleArguments().load_model: time.sleep(0.5) - time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) @@ -146,7 +146,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_sync_cart.pt") + gnet = torch.load("./cart_save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -182,7 +182,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a2c_sync_cart.pt") + torch.save(gnet, "./cart_save_model/a2c_sync_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 68d259d..27a7a74 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -106,13 +106,12 @@ def run(self): if done or ep_r == 600: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - end = time.time() + time_done = end - start + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) if not handleArguments().load_model: time.sleep(0.5) - time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index ec59c1d..48debf4 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -110,7 +110,7 @@ def run(self): if done or ep_r == 600: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) end = time.time() time_done = end - start @@ -145,7 +145,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart.pt") + gnet = torch.load("./cart_save_model/a3c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -184,7 +184,7 @@ def run(self): [w.join() for w in workers] if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a3c_cart.pt") + torch.save(gnet, "./cart_save_model/a3c_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index bc4b1b8..6a9f516 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -111,7 +111,7 @@ def run(self): if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) buffer_s, buffer_a, buffer_r = [], [], [] if done or ep_r == 600: # done and print information @@ -146,7 +146,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a3c_cart_comb.pt") + gnet = torch.load("./cart_save_model/a3c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -186,7 +186,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a3c_cart_comb.pt") + torch.save(gnet, "./cart_save_model/a3c_cart_comb.pt") elif handleArguments().load_model: print ("Testing! 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z&%%U|Z)~f@8?G_*I-fPj2FDdDLR#+u+Ti8IDqT#mGfSD}SobTry$eWDG764=nSoPB z#n3VD`%p2_7_P59%My%s)3E0w$ss8Werufu4mR5SN89}*)4)KpG6g11^XHaGC(sPJ zWNa5NgxnuCr1ww~?Y&K~?&}qCNyIQXajlr<8SKIk=aKx_WzKjtKoyo}9W5bG^AXDp0Es3Y&}C&pN>+teH?uEd`XI4MQrY* z1r+vH6@zOR!svRb@`yj2!1wV4dbuH(j7lW_ogwEZHiA}-vM?{nmvWa^z_@H#;hLE} zaKB5I)~;GdLqtxGVA@nkVcxh)w9L+mbgUF9dt47Btnekt89A6^DJ>W`x)whvM#J1_ zo&tiEgtsKO(5EAtsGzXv-$2ED5_tPZfYZ(S5b?`$<~gko(stee*Rip@$E#FW=UxE4 z{o_~bz)kRgSwpv1CLA6049tjYgl+Oo&?R92%c@gA;Z;4887vPP!WIZU7Y>ClPQq=s O9FY8H)Y98IM))6rz8u;B diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index e42fde7..4bd671e 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -4,6 +4,8 @@ from torch import nn import torch +import torch.multiprocessing as mp +import time import numpy as np import matplotlib.pyplot as plt import pickle @@ -22,7 +24,7 @@ def set_init(layers): nn.init.constant_(layer.bias, 0.) -def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): +def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma, synchronous, global_ep): if done: v_s_ = 0. # terminal else: @@ -46,8 +48,15 @@ def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): gp._grad = lp.grad opt.step() - # pull global parameters - lnet.load_state_dict(gnet.state_dict()) + if synchronous and not handleArguments().load_model: + # Pending for remaining agents to finish + while True: + if global_ep.value % mp.cpu_count() == 0: + lnet.load_state_dict(gnet.state_dict()) + break + else: + # pull global parameters + lnet.load_state_dict(gnet.state_dict()) def optimize(opt, lnet, done, s_, bs, ba, br, gamma): diff --git a/CARTPOLE/shared_adam.py b/CARTPOLE/shared_adam.py index e69de29..082fa69 100644 --- a/CARTPOLE/shared_adam.py +++ b/CARTPOLE/shared_adam.py @@ -0,0 +1,22 @@ +""" +Shared optimizer, the parameters in the optimizer will shared in the multiprocessors. +""" + +import torch + + +class SharedAdam(torch.optim.Adam): + def __init__(self, params, lr=1e-3, betas=(0.9, 0.99), eps=1e-8, + weight_decay=0): + super(SharedAdam, self).__init__(params, lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + # State initialization + for group in self.param_groups: + for p in group['params']: + state = self.state[p] + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p.data) + state['exp_avg_sq'] = torch.zeros_like(p.data) + + # share in memory + state['exp_avg'].share_memory_() + state['exp_avg_sq'].share_memory_() \ No newline at end of file From a5179b2f80fe41b8c1d9d8b9687296a6aa737a57 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 12 Apr 2020 01:27:48 +0200 Subject: [PATCH 29/63] VIZDOOM - reinitialize state within each episode, adjust plotter boundaries --- VIZDOOM/viz_utils.py | 2 +- VIZDOOM/vizdoom_a2c.py | 4 ++-- VIZDOOM/vizdoom_a2c_sync.py | 5 +++-- VIZDOOM/vizdoom_a3c.py | 10 ++++++---- 4 files changed, 12 insertions(+), 9 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 59c2e7a..88fdf64 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -100,7 +100,7 @@ def plotter_ep_rew(ax2, scores): def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,20) + ax1.set_ylim(0,200) ax1.set_ylabel('Duration of Episode') diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 731e0f5..32dfc39 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -17,7 +17,7 @@ GAMMA = 0.9 -MAX_EP = 100 +MAX_EP = 3000 frame_repeat = 12 resolution = (30, 45) config_file_path = "deadly_corridor.cfg" @@ -208,7 +208,7 @@ def loss_func(self, s, a, v_t): stop_processes = True break - s = s_ + state = s_ total_step += 1 diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index 6d55511..ff07475 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -140,6 +140,7 @@ def run(self): while self.g_ep.value < MAX_EP and stop_processes is False: self.game.new_episode() + state = game_state(game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: @@ -180,7 +181,7 @@ def run(self): stop_processes = True break - s = s_ + state = s_ total_step += 1 self.time_queue.put(None) @@ -249,7 +250,7 @@ def run(self): endtime = datetime.now() timedelta = endtime - starttime - print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) timedelta_sum += timedelta / 3 # Get results for confidence intervall diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index c77528b..6003ba0 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -19,7 +19,7 @@ GAMMA = 0.9 -MAX_EP = 30 +MAX_EP = 3000 frame_repeat = 12 resolution = (30, 45) config_file_path = "deadly_corridor.cfg" @@ -140,6 +140,7 @@ def run(self): while self.g_ep.value < MAX_EP and stop_processes is False: self.game.new_episode() + state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: @@ -179,7 +180,7 @@ def run(self): stop_processes = True break - s = s_ + state = s_ total_step += 1 self.time_queue.put(None) @@ -207,8 +208,9 @@ def run(self): model.eval() else: model = Net(len(actions)) - opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer + # global optimizer + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() # parallel training @@ -248,7 +250,7 @@ def run(self): endtime = datetime.now() timedelta = endtime - starttime - print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) timedelta_sum += timedelta / 3 # Get results for confidence intervall From e403c8438dc20a4378c5ba14ec9437a179ca39f0 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 01:20:40 +0200 Subject: [PATCH 30/63] CARTPOLE - remove normalizing reward --- .../__pycache__/cart_utils.cpython-36.pyc | Bin 4582 -> 4519 bytes CARTPOLE/a2c_cart.py | 8 ++-- CARTPOLE/a2c_cart_comb.py | 14 +++---- CARTPOLE/a2c_sync_cart.py | 9 ++--- CARTPOLE/a2c_sync_cart_comb.py | 8 ++-- CARTPOLE/a3c_cart.py | 8 ++-- CARTPOLE/a3c_cart_comb.py | 35 ++++++++---------- CARTPOLE/cart_save_model/a3c_cart.pt | Bin 12954 -> 12988 bytes CARTPOLE/cart_utils.py | 7 +--- 9 files changed, 39 insertions(+), 50 deletions(-) diff --git a/CARTPOLE/__pycache__/cart_utils.cpython-36.pyc b/CARTPOLE/__pycache__/cart_utils.cpython-36.pyc index ea2137109642633af9797d91873ed5c06736e6cb..7d064d1cd7f9cd50f1aa732e0803e32188b1110e 100644 GIT binary patch delta 138 zcmaE+yj+>fn3tEUYQf~VT_PK~#5fomCM$C2u<|f6)G$ph;xJZcs$s}tOku8J%wiN{ zNMR{qsbNiFZDwj_V`NBS2xib^^D7czU|^X1oMQ*C0Fc4J$i*lD!~&ByaJDjiVVrEv p)xelIc?Z`~#>~xi+&;{V`HN5UF)>cud`3W+k)4}^gM*Dr2>`#sAIks$ delta 201 zcmZ3k{7jk4n3tEUPk&OJx#&hNF%HHBlNC91RCO2`Y8bN^Q<$=uitK8bY8c}g^F+WR z%pwdmOeriP4B`xvgE)+xSZf%7>exVXVhkzlB`h_pDICpA&1{SeDGb33nw)+`A`A=+ z=|Bty>FeAdR*9^8Z=YT*0%Eq= 450: # update network # sync optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] @@ -157,7 +157,7 @@ def loss_func(self, s, a, v_t): if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./cart_save_model/a2c_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -182,7 +182,7 @@ def loss_func(self, s, a, v_t): 'weight': 'normal', 'size': 8 } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Cartpole", fontsize = 16) plt.show() diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index e26f82f..cf79534 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -90,7 +90,7 @@ def loss_func(self, s, a, v_t): if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./cart_save_model/a2c_cart_comb.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -124,17 +124,13 @@ def loss_func(self, s, a, v_t): buffer_s.append(s) buffer_r.append(r) - if done or ep_r == 600: # update network + if done or ep_r >= 400: # update network # sync optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] global_ep += 1 - - if global_ep_r == 0.: - global_ep_r = ep_r - else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_ep_r = ep_r end = time.time() duration = end - start @@ -155,7 +151,7 @@ def loss_func(self, s, a, v_t): if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./cart_save_model/a2c_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart_comb.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -180,7 +176,7 @@ def loss_func(self, s, a, v_t): 'weight': 'normal', 'size': 8 } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize = 16) plt.show() diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index 5f295e1..828dc73 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -19,7 +19,6 @@ import os os.environ["OMP_NUM_THREADS"] = "1" - GAMMA = 0.9 MAX_EP = 3000 @@ -106,7 +105,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r == 600: # update global and assign to local net + if done or ep_r >= 400: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -146,7 +145,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./cart_save_model/a2c_sync_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -182,7 +181,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./cart_save_model/a2c_sync_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -209,7 +208,7 @@ def run(self): 'weight': 'normal', 'size': 8, } - plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) plt.title("Synchronous A2C-Cartpole", fontsize = 16) plt.show() diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 27a7a74..880ac18 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -104,7 +104,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r == 600: # update global and assign to local net + if done or ep_r >= 400: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -143,7 +143,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./save_model/a2c_sync_cart_comb.pt") + gnet = torch.load("./CARTPOLE/save_model/a2c_sync_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -179,7 +179,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./save_model/a2c_sync_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/save_model/a2c_sync_cart_comb.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -205,7 +205,7 @@ def run(self): 'weight': 'normal', 'size': 8, } - plt.text(0, 650, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize = 16) plt.show() diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index 48debf4..c5f5e17 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -108,7 +108,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r == 600: # update global and assign to local net + if done or ep_r >= 400: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) end = time.time() @@ -145,7 +145,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./cart_save_model/a3c_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -184,7 +184,7 @@ def run(self): [w.join() for w in workers] if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./cart_save_model/a3c_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -211,7 +211,7 @@ def run(self): 'weight': 'normal', 'size': 8, } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Cartpole") plt.show() diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 6a9f516..662b8d3 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -19,7 +19,6 @@ import os os.environ["OMP_NUM_THREADS"] = "1" -UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 MAX_EP = 3000 @@ -109,23 +108,21 @@ def run(self): buffer_r.append(r) - if total_step % UPDATE_GLOBAL_ITER == 0 or done or ep_r == 600: # update global and assign to local net + if done or ep_r >= 400: # update global and assign to local net # sync push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) - buffer_s, buffer_a, buffer_r = [], [], [] - - if done or ep_r == 600: # done and print information - end = time.time() - time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: - stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - break + + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + if handleArguments().load_model: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + stop_processes = True + else: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + break s = s_ total_step += 1 self.time_queue.put(None) @@ -146,7 +143,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./cart_save_model/a3c_cart_comb.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -186,7 +183,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./cart_save_model/a3c_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart_comb.pt") elif handleArguments().load_model: print ("Testing! No need to save model.") else: @@ -213,7 +210,7 @@ def run(self): 'weight': 'normal', 'size': 8, } - plt.text(0, 650, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Cartpole (shared NN)") plt.show() diff --git a/CARTPOLE/cart_save_model/a3c_cart.pt b/CARTPOLE/cart_save_model/a3c_cart.pt index f750d0681223a8b70e4734874e1d5015312ecbcc..1ae547c3eb8cc596535bf978b76ac9b970995488 100644 GIT binary patch delta 6733 zcmZ9Qc{rC_*T)S>6qPBFArWPsr+cqoI7Q-UKp9FYp-?JH$&Zq;ktCU_qf(lL68Bn4 zLX$`%%^DF6nofh)^VE6Y=Xvk{_UBsHz4!OKuWMi5we|$@LEDc^auE>`F%DT49%Q^E zDs*9xsfmg4teA*}AuE z3Tq3?fuc;GQNMOEH8Z!cv9YqqJU;4og_*65wY8~P=6vN}|1hz%w6?M}v&k%1?hyV< z%ReAebdKzRKeiVChQCi6DfS=m->L^hMvDJ`IABHOp#Oru&J`)~U+|Zl$ie?W;lHf> zed0*Tf9SvAFDoL4{1^NsB~t1?;cuTKrT-KDwl;F;f5PA9MGgxx5*-ja@Cilaw#f^W ze=S5lz23+N3PwRihc|9t6^+*nYGI00Aok5j;&y+2fZ8k1V*YA%6xsWck8&=tdv5c= 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z-lyZ?h%wb(wJ&H@yb3qfMUHlEw5pbsdP4IvqVP?c0rYD)4igXTfE>ksG`KMsf`=8s zMbWc&;L&G7nQiNN9fexRx%)w|%s?4hhV`&@tq2rkw46lt%Iyb7-<;4esgR z#hK18!jKeM;TOGP*retGyN(&NY@HrZE+~Sx(|thH9fZ5b4yeBBYFVwcW()Hm1JbZu zSZ$j;vU>mLMy4U#z~pxf6pHrGkP%Kv+02Vp6re^|6})LY4N+^uSlQJwRBYbI%CF61 z1J>UZTR3$y`|9^#khB%LKaZoI=Gb)pDz)8MQysw4%VieYjxdCG5}`3v(|&gVd5r jXfUvX?okm;`+Y45yf2fQ_6R&`e-Jm?J)`pruh9PhXQ%@= diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index 4bd671e..2f0eb19 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -87,10 +87,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, ac with global_ep.get_lock(): global_ep.value += 1 with global_ep_r.get_lock(): - if global_ep_r.value == 0.: - global_ep_r.value = ep_r - else: - global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 + global_ep_r.value = ep_r res_queue.put(global_ep_r.value) time_queue.put(time_done) action_queue.put(a) @@ -100,7 +97,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, ac def plotter_ep_rew(ax2, scores): ax2.plot(scores) ax2.axhline(y=200.00, color='r') - ax2.set_ylim(0,700) + ax2.set_ylim(0,500) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') From 2026ba860dcb19828bf1c52b70067e33acb0056f Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 01:39:32 +0200 Subject: [PATCH 31/63] VIZDOOM - UPDATE_GLOBAL_ITER = 5 for A3C, correct error in push and pull --- VIZDOOM/viz_utils.py | 3 +-- VIZDOOM/vizdoom_a3c.py | 60 ++++++++++++++++++++++-------------------- 2 files changed, 33 insertions(+), 30 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 88fdf64..53196bb 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -26,8 +26,7 @@ def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): if done: v_s_ = 0. # terminal else: - v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] - + v_s_ = lnet.forward(s_)[1].data.numpy()[0, 0] buffer_v_target = [] for r in br[::-1]: # reverse buffer r diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 6003ba0..c52497e 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -17,12 +17,13 @@ import os os.environ["OMP_NUM_THREADS"] = "1" - +UPDATE_GLOBAL_ITER = 5 GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 10 frame_repeat = 12 resolution = (30, 45) -config_file_path = "deadly_corridor.cfg" +config_file_path = "VIZDOOM/deadly_corridor.cfg" +worker_num = int(mp.cpu_count()/3) # Number of reincarnations for Agent def initialize_vizdoom(config): @@ -53,6 +54,7 @@ def game_state(game): print("Action Size: ", n) print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +print("Number of used CPUs: ", worker_num) class Net(nn.Module): def __init__(self, a_dim): @@ -160,25 +162,27 @@ def run(self): buffer_s.append(state) buffer_r.append(r) - if done or ep_r == 600: # update network + if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network # sync push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) game.get_total_reward() - #buffer_s, buffer_a, buffer_r = [], [], [] - end = time.time() - time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, - self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - - # TODO: check for reasonable reward and adjust - if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: - stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: - stop_processes = True - break + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + + # TODO: check for reasonable reward and adjust + if handleArguments().load_model: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 10: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 10: + stop_processes = True + break state = s_ total_step += 1 @@ -204,7 +208,7 @@ def run(self): # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./doom_save_model/a3c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a3c_doom.pt") model.eval() else: model = Net(len(actions)) @@ -219,7 +223,7 @@ def run(self): [w.start() for w in workers] else: workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in - range(mp.cpu_count())] + range(worker_num)] [w.start() for w in workers] # record episode-reward and duration-episode to plot @@ -240,13 +244,13 @@ def run(self): [w.join() for w in workers] # TODO: check for reasonable reward and adjust - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./doom_save_model/a2c_doom.pt") - elif handleArguments().load_model: - print("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") + #if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") + #elif handleArguments().load_model: + # print("Testing! No need to save model.") + #else: + # print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime From 42e27f5f37696a5317e12cfd8099b538acb1e0a5 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 14:27:16 +0200 Subject: [PATCH 32/63] CARTPOLE - normalized and raw duration and reward plotting --- CARTPOLE/a2c_cart.py | 44 ++++++++++++----- CARTPOLE/a2c_cart_comb.py | 63 ++++++++++++++++-------- CARTPOLE/a2c_sync_cart.py | 39 ++++++++++----- CARTPOLE/a2c_sync_cart_comb.py | 65 ++++++++++++++++--------- CARTPOLE/a3c_cart.py | 82 ++++++++++++++++++------------- CARTPOLE/a3c_cart_comb.py | 88 ++++++++++++++++++++-------------- CARTPOLE/cart_utils.py | 31 ++++++++++-- 7 files changed, 269 insertions(+), 143 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index cc980df..4906212 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F from shared_adam import SharedAdam @@ -85,8 +85,13 @@ def loss_func(self, s, a, v_t): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) actions = [] - # Reinitialize Agent to make 5 trials - for i in range(3): + + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + + for i in range(runs): starttime = datetime.now() # load global network @@ -102,7 +107,7 @@ def loss_func(self, s, a, v_t): # Global variables for episodes durations = [] scores = [] - global_ep, global_ep_r = 1, 0. + global_ep, global_ep_r, global_time_done = 1, 0., 0. name = 'w00' total_step = 1 stop_processes = False @@ -131,19 +136,28 @@ def loss_func(self, s, a, v_t): optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] + end = time.time() + duration = end - start + global_ep += 1 - if global_ep_r == 0.: - global_ep_r = ep_r + if handleArguments().normalized_plot: + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_time_done = global_time_done * 0.99 + duration * 0.01 + print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, + "| Normalized Duration:", round(global_time_done, 5)) else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_ep_r = ep_r + print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + "| Duration:", + round(global_time_done, 5)) - end = time.time() - duration = end - start durations.append(duration) - - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) scores.append(int(global_ep_r)) + if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True @@ -175,8 +189,12 @@ def loss_func(self, s, a, v_t): confidence_intervall(actions) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, scores) + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, scores) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index cf79534..6a76ecf 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F from shared_adam import SharedAdam @@ -77,30 +77,36 @@ def loss_func(self, s, a, v_t): if __name__ == "__main__": - # load global network - print ("Starting A2C Agent for Cartpole-v0") + + print("Starting A2C Agent for Cartpole-v0") time.sleep(3) timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) actions = [] - # Reinitialize Agent to make 5 trials - for i in range(3): + + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + + for i in range(runs): starttime = datetime.now() + # load global network if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a2c_cart_comb.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer # Global variables for episodes durations = [] scores = [] - global_ep, global_ep_r = 1, 0. + global_ep, global_ep_r, global_time_done = 1, 0., 0. name = 'w00' total_step = 1 stop_processes = False @@ -124,20 +130,33 @@ def loss_func(self, s, a, v_t): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update network + if done or ep_r >= 450: # update network # sync optimize(opt, gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) buffer_s, buffer_a, buffer_r = [], [], [] - global_ep += 1 - global_ep_r = ep_r - end = time.time() duration = end - start - durations.append(duration) - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + global_ep += 1 + + if handleArguments().normalized_plot: + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_time_done = global_time_done * 0.99 + duration * 0.01 + print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, + "| Normalized Duration:", round(global_time_done, 5)) + else: + global_ep_r = ep_r + print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + "| Duration:", + round(global_time_done, 5)) + + durations.append(duration) scores.append(int(global_ep_r)) + if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True @@ -151,16 +170,16 @@ def loss_func(self, s, a, v_t): if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart.pt") elif handleArguments().load_model: - print ("Testing! No need to save model.") + print("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 + timedelta_sum += timedelta / 3 # Get results for confidence intervall if handleArguments().load_model: @@ -169,15 +188,19 @@ def loss_func(self, s, a, v_t): confidence_intervall(actions) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, scores) + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, scores) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize = 16) + plt.title("Vanilla A2C-Cartpole", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index 828dc73..874527f 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -77,13 +77,14 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped + self.lnet = Net(len(actions)) # local network + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.env = gym.make("CartPole-v0").unwrapped def run(self): total_step = 1 @@ -115,7 +116,8 @@ def run(self): if not handleArguments().load_model: time.sleep(0.5) - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: @@ -140,8 +142,13 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + # Reinitialize Agent to make 5 trials - for i in range(3): + for i in range(runs): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) @@ -152,14 +159,14 @@ def run(self): gnet.share_memory() # share the global parameters in multiprocessing opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [Worker(gnet, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] [w.start() for w in workers] else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + workers = [Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] [w.start() for w in workers] # record episode-reward and episode-duration to plot @@ -172,7 +179,9 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: actions.append(a) else: break @@ -200,8 +209,12 @@ def run(self): confidence_intervall(actions) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) font = {'family': 'serif', 'color': 'darkred', diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 880ac18..cc95392 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -76,13 +76,14 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue, self.time_queue, self.action_queue = global_ep, global_ep_r, res_queue, time_queue, action_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped + self.lnet = Net(len(actions)) # local network + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.env = gym.make("CartPole-v0").unwrapped def run(self): total_step = 1 @@ -108,18 +109,22 @@ def run(self): # sync end = time.time() time_done = end - start - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) + + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, + self.g_ep) if not handleArguments().load_model: time.sleep(0.5) - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue,self.time_queue, time_done, a, self.action_queue, self.name) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True break @@ -138,26 +143,35 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + # Reinitialize Agent to make 5 trials - for i in range(3): + for i in range(runs): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/save_model/a2c_sync_cart_comb.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) - gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(1)] [w.start() for w in workers] else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(mp.cpu_count())] [w.start() for w in workers] # record episode-reward and episode-duration to plot @@ -170,7 +184,9 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: actions.append(a) else: break @@ -179,26 +195,31 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/save_model/a2c_sync_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") elif handleArguments().load_model: - print ("Testing! No need to save model.") + print("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 + timedelta_sum += timedelta / 3 # Get results for confidence intervall + if handleArguments().load_model: confidence_intervall(actions, True) else: confidence_intervall(actions) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) font = {'family': 'serif', 'color': 'darkred', @@ -206,7 +227,7 @@ def run(self): 'size': 8, } plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize = 16) + plt.title("Synchronous A2C-Cartpole", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index c5f5e17..afbe93d 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -78,20 +78,19 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - self.time_queue, self.action_queue = time_queue, action_queue + self.lnet = Net(len(actions)) # local network + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.env = gym.make("CartPole-v0").unwrapped def run(self): total_step = 1 stop_processes = False scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] @@ -110,11 +109,13 @@ def run(self): if done or ep_r >= 400: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, + self.g_ep) + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: @@ -128,45 +129,51 @@ def run(self): s = s_ total_step += 1 - self.time_queue.put(None) self.res_queue.put(None) + self.time_queue.put(None) self.action_queue.put(None) + if __name__ == "__main__": # load global network - print("STARTING A3C AGENT FOR CARTPOLE-V0") + print("Starting Synchronous A2C Agent for Cartpole-v0") time.sleep(3) timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + # Reinitialize Agent to make 5 trials - for i in range(3): + for i in range(runs): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) - # share the global parameters in multiprocessing - gnet.share_memory() - - # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) - - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(1)] [w.start() for w in workers] else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(mp.cpu_count())] [w.start() for w in workers] - # record episode-reward and duration-episode to plot + # record episode-reward and episode-duration to plot res = [] durations = [] actions = [] @@ -176,43 +183,50 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: actions.append(a) else: break [w.join() for w in workers] + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") elif handleArguments().load_model: - print ("Testing! No need to save model.") + print("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") - endtime = datetime.now() timedelta = endtime - starttime print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 + + timedelta_sum += timedelta / 3 # Get results for confidence intervall + if handleArguments().load_model: confidence_intervall(actions, True) else: confidence_intervall(actions) - # Plot results after each run - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + # Plot results + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) - # Finish plots font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8, } - plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Cartpole") + plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 662b8d3..5cf1881 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn -from cart_utils import v_wrap, set_init, push_and_pull, record, plotter_ep_rew, handleArguments, plotter_ep_time, confidence_intervall +from cart_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import matplotlib.pyplot as plt import torch.nn.functional as F import torch.multiprocessing as mp @@ -77,20 +77,19 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(N_S, N_A) # local network - self.env = gym.make('CartPole-v0').unwrapped - self.time_queue, self.action_queue = time_queue, action_queue + self.lnet = Net(len(actions)) # local network + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.env = gym.make("CartPole-v0").unwrapped def run(self): total_step = 1 stop_processes = False scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: s = self.env.reset() buffer_s, buffer_a, buffer_r = [], [], [] @@ -107,64 +106,73 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update global and assign to local net # sync - push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, self.g_ep) - end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, self.action_queue, self.name) + + push_and_pull(self.opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, False, + self.g_ep) + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) if handleArguments().load_model: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[ + -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True break + s = s_ total_step += 1 - self.time_queue.put(None) self.res_queue.put(None) + self.time_queue.put(None) self.action_queue.put(None) if __name__ == "__main__": # load global network - print("STARTING A3C AGENT FOR CARTPOLE-V0") + print("Starting Synchronous A2C Agent for Cartpole-v0") time.sleep(3) timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + # Reinitialize Agent to make 5 trials - for i in range(3): + for i in range(runs): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart_comb.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) - # share the global parameters in multiprocessing - gnet.share_memory() - - # global optimizer - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) - - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - + gnet.share_memory() # share the global parameters in multiprocessing + opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(1)] [w.start() for w in workers] else: - workers = [Worker(gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(mp.cpu_count())] + workers = [ + Worker(gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for + i in range(mp.cpu_count())] [w.start() for w in workers] - # record episode-reward and duration-episode to plot + # record episode-reward and episode-duration to plot res = [] durations = [] actions = [] @@ -174,7 +182,9 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: actions.append(a) else: break @@ -183,35 +193,39 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart_comb.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") elif handleArguments().load_model: - print ("Testing! No need to save model.") + print("Testing! No need to save model.") else: print("Failed to train agent. Model was not saved") - endtime = datetime.now() timedelta = endtime - starttime print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta/3 + + timedelta_sum += timedelta / 3 # Get results for confidence intervall + if handleArguments().load_model: confidence_intervall(actions, True) else: confidence_intervall(actions) - # Plot results after each run - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + # Plot results + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) - # Finish plots font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8, } - plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Cartpole (shared NN)") + plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C-Cartpole", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index 2f0eb19..34ff032 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -83,16 +83,33 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): opt.step() -def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, action_queue, name): +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done, time_done, a, action_queue, name): with global_ep.get_lock(): global_ep.value += 1 - with global_ep_r.get_lock(): - global_ep_r.value = ep_r + + if handleArguments().normalized_plot: + with global_ep_r.get_lock(): + if global_ep_r.value == 0.: + global_ep_r.value = ep_r + else: + global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 + time_done = global_time_done.value * 0.99 + time_done * 0.01 + print(name, "Ep:", global_ep.value, "| Normalized Reward: %.0f" % global_ep_r.value, "| Normalized Duration:", round(time_done, 5)) + else: + with global_ep_r.get_lock(): + global_ep_r.value = ep_r + print(name, "Ep:", global_ep.value, "| Epsidode Reward: %.0f" % global_ep_r.value, "| Duration:", + round(time_done, 5)) res_queue.put(global_ep_r.value) time_queue.put(time_done) action_queue.put(a) - print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value, "| Duration:", round(time_done, 5)) +def plotter_ep_rew_norm(ax2, scores): + ax2.plot(scores) + ax2.axhline(y=200.00, color='r') + ax2.set_ylim(0,500) + ax2.set_ylabel('Reward per Episode') + ax2.set_xlabel('Episode') def plotter_ep_rew(ax2, scores): ax2.plot(scores) @@ -101,6 +118,11 @@ def plotter_ep_rew(ax2, scores): ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') +def plotter_ep_time_norm(ax1, duration_episode): + ax1.plot(duration_episode) + ax1.set_ylim(0,0.02) + ax1.set_ylabel('Duration of Episode') + def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) @@ -148,6 +170,7 @@ def handleArguments(): description="Switch between modes in A2C or loading models from previous games") parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + parser.add_argument("--normalized_plot", "-n", help="Shows plot of normalized reward", action="store_true") global args args = parser.parse_args() return args \ No newline at end of file From b0ae642cba0d4a90362785d61b8f74b473189e6e Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 14:30:07 +0200 Subject: [PATCH 33/63] VIZDOOM - normalized and raw reward and duration, update plotter boundaries --- VIZDOOM/viz_utils.py | 36 ++++++++++---- VIZDOOM/vizdoom_a2c.py | 57 ++++++++++++++-------- VIZDOOM/vizdoom_a2c_sync.py | 95 ++++++++++++++++++++++--------------- VIZDOOM/vizdoom_a3c.py | 66 +++++++++++++++----------- 4 files changed, 160 insertions(+), 94 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 53196bb..3f7ccdc 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -75,31 +75,50 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): opt.step() -def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, time_done, a, action_queue, name): +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done, time_done, a, action_queue, name): with global_ep.get_lock(): global_ep.value += 1 - with global_ep_r.get_lock(): - if global_ep_r.value == 0.: + + if handleArguments().normalized_plot: + with global_ep_r.get_lock(): + if global_ep_r.value == 0.: + global_ep_r.value = ep_r + else: + global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 + time_done = global_time_done.value * 0.99 + time_done * 0.01 + print(name, "Ep:", global_ep.value, "| Normalized Reward: %.0f" % global_ep_r.value, "| Normalized Duration:", round(time_done, 5)) + else: + with global_ep_r.get_lock(): global_ep_r.value = ep_r - else: - global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 + print(name, "Ep:", global_ep.value, "| Epsidode Reward: %.0f" % global_ep_r.value, "| Duration:", + round(time_done, 5)) res_queue.put(global_ep_r.value) time_queue.put(time_done) action_queue.put(a) - print(name, "Ep:", global_ep.value, "| Ep_r: %.0f" % global_ep_r.value, "| Duration:", round(time_done, 5)) +def plotter_ep_rew_norm(ax2, scores): + ax2.plot(scores) + #ax2.axhline(y=200.00, color='r') + ax2.set_ylim(-120,70) + ax2.set_ylabel('Reward per Episode') + ax2.set_xlabel('Episode') + def plotter_ep_rew(ax2, scores): ax2.plot(scores) #ax2.axhline(y=200.00, color='r') - ax2.set_ylim(-150,300) + ax2.set_ylim(-150,450) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') +def plotter_ep_time_norm(ax1, duration_episode): + ax1.plot(duration_episode) + ax1.set_ylim(0,0.1) + ax1.set_ylabel('Duration of Episode') def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,200) + ax1.set_ylim(0,20) ax1.set_ylabel('Duration of Episode') @@ -144,6 +163,7 @@ def handleArguments(): description="Switch between modes in A2C or loading models from previous games") parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") + parser.add_argument("--normalized_plot", "-n", help="Shows plot of normalized reward", action="store_true") global args args = parser.parse_args() return args diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 32dfc39..3667515 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -10,14 +10,14 @@ from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import set_init, plotter_ep_rew, handleArguments, optimize, plotter_ep_time, confidence_intervall +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall from shared_adam import SharedAdam import os os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 1000 frame_repeat = 12 resolution = (30, 45) config_file_path = "deadly_corridor.cfg" @@ -135,7 +135,12 @@ def loss_func(self, s, a, v_t): fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) action = [] - for i in range(3): + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + + for i in range(runs): starttime = datetime.now() # load global network @@ -151,7 +156,7 @@ def loss_func(self, s, a, v_t): # Global variables for episodes durations = [] scores = [] - global_ep, global_ep_r = 1, 0. + global_ep, global_ep_r, global_time_done = 1, 0., 0. name = 'w00' total_step = 1 stop_processes = False @@ -186,33 +191,38 @@ def loss_func(self, s, a, v_t): print("Total Reward:", game.get_total_reward()) buffer_s, buffer_a, buffer_r = [], [], [] - global_ep += 1 - if global_ep_r == 0.: - global_ep_r = ep_r - else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - end = time.time() duration = end - start - durations.append(duration) - print("w00 Ep:", global_ep, "| Ep_r: %.0f" % global_ep_r, "| Duration:", round(duration, 5)) + global_ep += 1 + if handleArguments().normalized_plot: + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_time_done = global_time_done * 0.99 + duration * 0.01 + print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, + "| Normalized Duration:", round(global_time_done, 5)) + else: + global_ep_r = ep_r + print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + "| Duration:", + round(global_time_done, 5)) + scores.append(int(global_ep_r)) + durations.append(global_time_done) - # TODO: check for reasonable reward and adjust if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 50: stop_processes = True else: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 50: stop_processes = True break state = s_ total_step += 1 - - # TODO: check for reasonable reward and adjust if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: print("Save model") torch.save(model, "./doom_save_model/a2c_doom.pt") @@ -233,15 +243,22 @@ def loss_func(self, s, a, v_t): # confidence_intervall(action) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, scores) + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, scores) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } - plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + if handleArguments().normalized_plot: + plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + else: + plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Vizdoom", fontsize = 16) plt.show() diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index ff07475..8cab98f 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -10,19 +10,20 @@ from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, push_and_pull, record, plotter_ep_time, confidence_intervall +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys import os os.environ["OMP_NUM_THREADS"] = "1" - +UPDATE_GLOBAL_ITER = 5 GAMMA = 0.9 -MAX_EP = 30 +MAX_EP = 2000 frame_repeat = 12 resolution = (30, 45) -config_file_path = "deadly_corridor.cfg" +config_file_path = "VIZDOOM/deadly_corridor.cfg" +worker_num = int(mp.cpu_count()) # Number of reincarnations for Agent def initialize_vizdoom(config): @@ -53,6 +54,7 @@ def game_state(game): print("Action Size: ", n) print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +print("Number of used CPUs: ", worker_num) class Net(nn.Module): def __init__(self, a_dim): @@ -124,14 +126,14 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt self.lnet = Net(len(actions)) # local network self.game = initialize_vizdoom(config_file_path) - self.time_queue, self.action_queue = time_queue, action_queue + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue def run(self): total_step = 1 @@ -140,7 +142,7 @@ def run(self): while self.g_ep.value < MAX_EP and stop_processes is False: self.game.new_episode() - state = game_state(game) + state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: @@ -160,26 +162,27 @@ def run(self): buffer_s.append(state) buffer_r.append(r) - if done or ep_r == 600: # update network + if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network # sync push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - time.sleep(10) + time.sleep(5) game.get_total_reward() - #buffer_s, buffer_a, buffer_r = [], [], [] - end = time.time() - time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, - self.action_queue, self.name) - scores.append(int(self.g_ep_r.value)) - - # TODO: check for reasonable reward and adjust - if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: - stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and self.g_ep.value >= 10: - stop_processes = True - break + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) + + if handleArguments().load_model: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + stop_processes = True + else: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + stop_processes = True + break state = s_ total_step += 1 @@ -197,29 +200,34 @@ def run(self): timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - action = [] - for i in range (3): + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + + for i in range(runs): starttime = datetime.now() # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./doom_save_model/a3c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a3c_doom.pt") model.eval() else: model = Net(len(actions)) - opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer - - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() + # global optimizer + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] [w.start() for w in workers] else: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in - range(mp.cpu_count())] + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in + range(worker_num)] [w.start() for w in workers] # record episode-reward and duration-episode to plot @@ -232,17 +240,18 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: action.append(a) else: break [w.join() for w in workers] - # TODO: check for reasonable reward and adjust if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: print("Save model") - torch.save(model, "./doom_save_model/a2c_doom.pt") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -260,15 +269,23 @@ def run(self): # confidence_intervall(action) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } - plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + if handleArguments().normalized_plot: + plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + else: + plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom", fontsize=16) plt.show() diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index c52497e..7d55754 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -10,7 +10,7 @@ from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, handleArguments, push_and_pull, record, plotter_ep_time, confidence_intervall +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -19,11 +19,11 @@ UPDATE_GLOBAL_ITER = 5 GAMMA = 0.9 -MAX_EP = 10 +MAX_EP = 2000 frame_repeat = 12 resolution = (30, 45) config_file_path = "VIZDOOM/deadly_corridor.cfg" -worker_num = int(mp.cpu_count()/3) # Number of reincarnations for Agent +worker_num = int(mp.cpu_count()) # Number of reincarnations for Agent def initialize_vizdoom(config): @@ -126,14 +126,14 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name - self.g_ep, self.g_ep_r, self.res_queue = global_ep, global_ep_r, res_queue + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt self.lnet = Net(len(actions)) # local network self.game = initialize_vizdoom(config_file_path) - self.time_queue, self.action_queue = time_queue, action_queue + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue def run(self): total_step = 1 @@ -171,16 +171,15 @@ def run(self): if done: end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, time_done, a, + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - # TODO: check for reasonable reward and adjust if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: stop_processes = True else: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 10: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: stop_processes = True break @@ -200,9 +199,13 @@ def run(self): timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - action = [] - for i in range (3): + if handleArguments().normalized_plot: + runs = 3 + else: + runs = 1 + + for i in range(runs): starttime = datetime.now() # load global network @@ -215,14 +218,14 @@ def run(self): # global optimizer opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) - global_ep, global_ep_r, res_queue, time_queue, action_queue = mp.Value('i', 0), mp.Value('d', 0.), mp.Queue(), mp.Queue(), mp.Queue() - + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in range(1)] + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] [w.start() for w in workers] else: - workers = [Worker(model, opt, global_ep, global_ep_r, res_queue, time_queue, action_queue, i) for i in + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in range(worker_num)] [w.start() for w in workers] @@ -236,21 +239,22 @@ def run(self): a = action_queue.get() if r is not None: res.append(r) + if t is not None: durations.append(t) + if a is not None: action.append(a) else: break [w.join() for w in workers] - # TODO: check for reasonable reward and adjust - #if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") - #elif handleArguments().load_model: - # print("Testing! No need to save model.") - #else: - # print("Failed to train agent. Model was not saved") + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime @@ -264,15 +268,23 @@ def run(self): # confidence_intervall(action) # Plot results - plotter_ep_time(ax1, durations) - plotter_ep_rew(ax2, res) + + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } - plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + if handleArguments().normalized_plot: + plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + else: + plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom", fontsize=16) plt.show() From 51978ff83a1c0d3e066256d9a6a9ab80ac11e9c1 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 18:38:52 +0200 Subject: [PATCH 34/63] CART/DOOM - turn off episode breaker for not normalized runs --- CARTPOLE/a2c_cart.py | 6 ++++-- CARTPOLE/a2c_cart_comb.py | 6 ++++-- CARTPOLE/a2c_sync_cart.py | 11 +++++++---- CARTPOLE/a2c_sync_cart_comb.py | 12 +++++++----- CARTPOLE/a3c_cart.py | 12 +++++++----- CARTPOLE/a3c_cart_comb.py | 12 +++++++----- VIZDOOM/vizdoom_a2c.py | 11 ++++++----- VIZDOOM/vizdoom_a2c_sync.py | 12 ++++++++---- VIZDOOM/vizdoom_a3c.py | 14 ++++++++++---- 9 files changed, 60 insertions(+), 36 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index 4906212..67a8674 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -158,12 +158,14 @@ def loss_func(self, s, a, v_t): durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - else: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index 6a76ecf..5460eb2 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -157,12 +157,14 @@ def loss_func(self, s, a, v_t): durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - else: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index 874527f..c445ee8 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -118,13 +118,16 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + elif handleArguments().normalized_plot: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index cc95392..e90b61f 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -118,14 +118,16 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + elif handleArguments().normalized_plot: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index afbe93d..7c1c13c 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -117,14 +117,16 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + elif handleArguments().normalized_plot: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 5cf1881..fe4962a 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -116,14 +116,16 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[ - -min(mp.cpu_count(), len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): + elif handleArguments().normalized_plot: + if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break s = s_ diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 3667515..c86ab7b 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -212,13 +212,14 @@ def loss_func(self, s, a, v_t): scores.append(int(global_ep_r)) durations.append(global_time_done) - if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 50: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 50 and global_ep >= 100: stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 0 and global_ep >= 50: + elif handleArguments().normalized_plot: + if np.mean(scores[-min(10, len(scores)):]) >= 50 and global_ep >= 10: stop_processes = True - break + else: + stop_processes = False state = s_ total_step += 1 diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index 8cab98f..bcdcc66 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -174,14 +174,18 @@ def run(self): time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + + scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + elif handleArguments().normalized_plot: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break state = s_ diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 7d55754..6ec1958 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -173,14 +173,20 @@ def run(self): time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) + + if not handleArguments().normalized_plot: + self.g_ep_r.value = 0.99 * self.g_ep_r.value + ep_r * 0.01 scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: stop_processes = True - else: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= 50: + elif handleArguments().normalized_plot: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): stop_processes = True + else: + stop_processes = False break state = s_ From 261cd3cef5518246bec2acbceb1980347da7e411 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 13 Apr 2020 23:16:57 +0200 Subject: [PATCH 35/63] CARTPOLE - correct coding mistakes in A3C and A2C-sync --- CARTPOLE/a2c_sync_cart.py | 10 +++++----- CARTPOLE/a2c_sync_cart_comb.py | 30 +++++++++++++++--------------- CARTPOLE/a3c_cart.py | 16 ++++++++-------- CARTPOLE/a3c_cart_comb.py | 30 +++++++++++++++--------------- 4 files changed, 43 insertions(+), 43 deletions(-) diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index c445ee8..f314b6e 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -20,7 +20,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -82,7 +82,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(len(actions)) # local network + self.lnet = Net(N_S, N_A) # local network self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.env = gym.make("CartPole-v0").unwrapped @@ -106,7 +106,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update global and assign to local net + if done or ep_r >= 450: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -121,10 +121,10 @@ def run(self): scores.append(int(self.g_ep_r.value)) if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True elif handleArguments().normalized_plot: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: stop_processes = False diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index e90b61f..5da7d8c 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -20,7 +20,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -32,11 +32,11 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) - self.pi2 = nn.Linear(48, 48) - self.pi3 = nn.Linear(48, a_dim) - self.v2 = nn.Linear(48, 48) - self.v3 = nn.Linear(48, 1) + self.pi1 = nn.Linear(s_dim, 80) + self.pi2 = nn.Linear(80, 60) + self.pi3 = nn.Linear(60, a_dim) + self.v2 = nn.Linear(80, 60) + self.v3 = nn.Linear(60, 1) set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -81,7 +81,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(len(actions)) # local network + self.lnet = Net(N_S, N_A) # local network self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.env = gym.make("CartPole-v0").unwrapped @@ -105,7 +105,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update global and assign to local net + if done or ep_r >= 450: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -121,10 +121,10 @@ def run(self): scores.append(int(self.g_ep_r.value)) if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True elif handleArguments().normalized_plot: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: stop_processes = False @@ -155,13 +155,13 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training @@ -195,9 +195,9 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 200 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart_comb.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -229,7 +229,7 @@ def run(self): 'size': 8, } plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole", fontsize=16) + plt.title("Synchronous A2C-Cartpole (shared NN)", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index 7c1c13c..ae6fad4 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -21,7 +21,7 @@ #UPDATE_GLOBAL_ITER = 20 GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -83,7 +83,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(len(actions)) # local network + self.lnet = Net(N_S, N_A) # local network self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.env = gym.make("CartPole-v0").unwrapped @@ -107,7 +107,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update global and assign to local net + if done or ep_r >= 450: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -120,10 +120,10 @@ def run(self): scores.append(int(self.g_ep_r.value)) if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True elif handleArguments().normalized_plot: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: stop_processes = False @@ -154,7 +154,7 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -196,7 +196,7 @@ def run(self): if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -228,7 +228,7 @@ def run(self): 'size': 8, } plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole", fontsize=16) + plt.title("A3C-Cartpole", fontsize=16) plt.show() sys.exit() diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index fe4962a..47b97c6 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -20,7 +20,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 @@ -34,11 +34,11 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 48) - self.pi2 = nn.Linear(48, 48) - self.pi3 = nn.Linear(48, a_dim) - self.v2 = nn.Linear(48, 48) - self.v3 = nn.Linear(48, 1) + self.pi1 = nn.Linear(s_dim, 80) + self.pi2 = nn.Linear(80, 60) + self.pi3 = nn.Linear(60, a_dim) + self.v2 = nn.Linear(80, 60) + self.v3 = nn.Linear(60, 1) set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -82,7 +82,7 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done self.gnet, self.opt = gnet, opt - self.lnet = Net(len(actions)) # local network + self.lnet = Net(N_S, N_A) # local network self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.env = gym.make("CartPole-v0").unwrapped @@ -106,7 +106,7 @@ def run(self): buffer_s.append(s) buffer_r.append(r) - if done or ep_r >= 400: # update global and assign to local net + if done or ep_r >= 450: # update global and assign to local net # sync end = time.time() time_done = end - start @@ -119,10 +119,10 @@ def run(self): scores.append(int(self.g_ep_r.value)) if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 10 and self.g_ep.value >= 100: + if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True elif handleArguments().normalized_plot: - if np.mean(scores[-min(10, len(scores)):]) >= 10 and self.g_ep.value >= mp.cpu_count(): + if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: stop_processes = False @@ -153,13 +153,13 @@ def run(self): starttime = datetime.now() if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a3c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) gnet.share_memory() # share the global parameters in multiprocessing - opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer + opt = SharedAdam(gnet.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() # parallel training @@ -193,9 +193,9 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 300 and not handleArguments().load_model: + if np.mean(res[-min(mp.cpu_count(), len(res)):]) >= 200 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_sync_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a3c_cart_comb.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -227,7 +227,7 @@ def run(self): 'size': 8, } plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Cartpole", fontsize=16) + plt.title("A3C-Cartpole (shared NN)", fontsize=16) plt.show() sys.exit() From 53beb17f5d70ea660c677f9386c2e562d4f23b29 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 14 Apr 2020 21:05:16 +0200 Subject: [PATCH 36/63] VIZDOOM - correct A2C and A3C, adjust plotter boundaries --- VIZDOOM/viz_utils.py | 8 ++-- VIZDOOM/vizdoom_a2c.py | 91 ++++++++++++++++++++++-------------------- VIZDOOM/vizdoom_a3c.py | 5 +-- 3 files changed, 53 insertions(+), 51 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 3f7ccdc..01e82d2 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -55,7 +55,7 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): if done: v_s_ = 0. # terminal else: - v_s_ = lnet.forward(v_wrap(s_[None, :]))[-1].data.numpy()[0, 0] + v_s_ = lnet.forward(s_)[1].data.numpy()[0, 0] buffer_v_target = [] @@ -100,20 +100,20 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done def plotter_ep_rew_norm(ax2, scores): ax2.plot(scores) #ax2.axhline(y=200.00, color='r') - ax2.set_ylim(-120,70) + ax2.set_ylim(-120,300) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') def plotter_ep_rew(ax2, scores): ax2.plot(scores) #ax2.axhline(y=200.00, color='r') - ax2.set_ylim(-150,450) + ax2.set_ylim(-120,550) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') def plotter_ep_time_norm(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.1) + ax1.set_ylim(0,2) ax1.set_ylabel('Duration of Episode') def plotter_ep_time(ax1, duration_episode): diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index c86ab7b..4b42e95 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -15,12 +15,12 @@ import os os.environ["OMP_NUM_THREADS"] = "1" - +UPDATE_MODEL = 5 GAMMA = 0.9 -MAX_EP = 1000 +MAX_EP = 2000 frame_repeat = 12 resolution = (30, 45) -config_file_path = "deadly_corridor.cfg" +config_file_path = "VIZDOOM/deadly_corridor.cfg" def initialize_vizdoom(config): @@ -146,7 +146,7 @@ def loss_func(self, s, a, v_t): # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./doom_save_model/a2c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a2c_doom.pt") model.eval() else: model = Net(len(actions)) @@ -161,11 +161,13 @@ def loss_func(self, s, a, v_t): total_step = 1 stop_processes = False - while global_ep < MAX_EP: + while global_ep < MAX_EP and stop_processes is False: game.new_episode() + state = game_state(game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. while True: + # Initialize stopwatch for average episode duration start = time.time() done = False @@ -184,53 +186,56 @@ def loss_func(self, s, a, v_t): buffer_s.append(state) buffer_r.append(r) - if done or ep_r == 50: # update network + if done or total_step % UPDATE_MODEL == 0: # update network # sync optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) #print("Acion_array:", action) print("Total Reward:", game.get_total_reward()) buffer_s, buffer_a, buffer_r = [], [], [] - end = time.time() - duration = end - start - - global_ep += 1 - if handleArguments().normalized_plot: - if global_ep_r == 0.: - global_ep_r = ep_r + if done: + end = time.time() + duration = end - start + + global_ep += 1 + if handleArguments().normalized_plot: + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_time_done = global_time_done * 0.99 + duration * 0.01 + print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, + "| Normalized Duration:", round(global_time_done, 5)) else: - global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 - global_time_done = global_time_done * 0.99 + duration * 0.01 - print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, - "| Normalized Duration:", round(global_time_done, 5)) - else: - global_ep_r = ep_r - print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, - "| Duration:", - round(global_time_done, 5)) - - scores.append(int(global_ep_r)) - durations.append(global_time_done) - - if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 50 and global_ep >= 100: - stop_processes = True - elif handleArguments().normalized_plot: - if np.mean(scores[-min(10, len(scores)):]) >= 50 and global_ep >= 10: - stop_processes = True - else: - stop_processes = False + global_ep_r = ep_r + global_time_done = duration + print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + "| Duration:", + round(global_time_done, 5)) + + scores.append(int(global_ep_r)) + durations.append(global_time_done) + + #if handleArguments().load_model and handleArguments().normalized_plot: + # if np.mean(scores[-min(100, len(scores)):]) >= 50 and global_ep >= 100: + # stop_processes = True + #elif handleArguments().normalized_plot: + # if np.mean(scores[-min(10, len(scores)):]) >= 50 and global_ep >= 10: + # stop_processes = True + #else: + # stop_processes = False + break state = s_ total_step += 1 - if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: - print("Save model") - torch.save(model, "./doom_save_model/a2c_doom.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") + #if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") + #elif handleArguments().load_model: + # print ("Testing! No need to save model.") + #else: + # print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime @@ -257,9 +262,9 @@ def loss_func(self, s, a, v_t): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) else: - plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Vizdoom", fontsize = 16) plt.show() diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 6ec1958..3286795 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -174,11 +174,8 @@ def run(self): record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) - if not handleArguments().normalized_plot: - self.g_ep_r.value = 0.99 * self.g_ep_r.value + ep_r * 0.01 scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: stop_processes = True @@ -290,7 +287,7 @@ def run(self): if handleArguments().normalized_plot: plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) else: - plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom", fontsize=16) plt.show() From 5dc7e25d027ee64fe1ee822ef51aee5797259ed5 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 14 Apr 2020 22:36:51 +0200 Subject: [PATCH 37/63] CARTPOLE - create save_model.py, enable save data mode for all --- CARTPOLE/a2c_cart.py | 21 +++++--- CARTPOLE/a2c_cart_comb.py | 18 ++++--- CARTPOLE/a2c_sync_cart.py | 15 ++++-- CARTPOLE/a2c_sync_cart_comb.py | 9 +++- CARTPOLE/a3c_cart.py | 9 +++- CARTPOLE/a3c_cart_comb.py | 9 +++- CARTPOLE/algo_compare.py | 97 ++++++++++++++++++++++++++++++++++ CARTPOLE/cart_utils.py | 5 +- 8 files changed, 159 insertions(+), 24 deletions(-) create mode 100644 CARTPOLE/algo_compare.py diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index 67a8674..73990ab 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -19,7 +19,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -84,6 +84,10 @@ def loss_func(self, s, a, v_t): timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Global variables for episodes + durations = [] + scores = [] actions = [] if handleArguments().normalized_plot: @@ -104,9 +108,6 @@ def loss_func(self, s, a, v_t): opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - # Global variables for episodes - durations = [] - scores = [] global_ep, global_ep_r, global_time_done = 1, 0., 0. name = 'w00' total_step = 1 @@ -151,17 +152,17 @@ def loss_func(self, s, a, v_t): "| Normalized Duration:", round(global_time_done, 5)) else: global_ep_r = ep_r - print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + print(name, "Ep:", global_ep, "| Epsisode Reward: %.0f" % global_ep_r, "| Duration:", round(global_time_done, 5)) durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True else: @@ -206,4 +207,10 @@ def loss_func(self, s, a, v_t): plt.title("Vanilla A2C-Cartpole", fontsize = 16) plt.show() + if handleArguments().save_data: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', scores, delimiter=',') + + + sys.exit() diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index 5460eb2..fae83cc 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -19,7 +19,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 3000 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -83,6 +83,10 @@ def loss_func(self, s, a, v_t): timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + # Global variables for episodes + durations = [] + scores = [] actions = [] if handleArguments().normalized_plot: @@ -103,9 +107,6 @@ def loss_func(self, s, a, v_t): opt = SharedAdam(gnet.parameters(), lr=0.005, betas=(0.92, 0.999)) # global optimizer - # Global variables for episodes - durations = [] - scores = [] global_ep, global_ep_r, global_time_done = 1, 0., 0. name = 'w00' total_step = 1 @@ -157,10 +158,10 @@ def loss_func(self, s, a, v_t): durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True else: @@ -205,4 +206,9 @@ def loss_func(self, s, a, v_t): plt.title("Vanilla A2C-Cartpole", fontsize=16) plt.show() + if handleArguments().save_data: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', scores, delimiter=',') + + sys.exit() diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index f314b6e..85f53df 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -20,7 +20,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 2000 +MAX_EP = 100 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -120,10 +120,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -219,6 +219,10 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + if handleArguments().save_data: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', scores, delimiter=',') + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -228,5 +232,10 @@ def run(self): plt.title("Synchronous A2C-Cartpole", fontsize = 16) plt.show() + if handleArguments().save_data: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', res, delimiter=',') + + sys.exit() diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 5da7d8c..9e2c7fa 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -120,10 +120,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -223,6 +223,11 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + if handleArguments().save_data: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', scores, delimiter=',') + + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index ae6fad4..9826a51 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -119,10 +119,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -222,6 +222,11 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + if handleArguments().save_data: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', scores, delimiter=',') + + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 47b97c6..975395e 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -118,10 +118,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: + if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot: + elif handleArguments().normalized_plot and not handleArguments().save_data: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -221,6 +221,11 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + if handleArguments().save_data: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', scores, delimiter=',') + + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', diff --git a/CARTPOLE/algo_compare.py b/CARTPOLE/algo_compare.py new file mode 100644 index 0000000..7769d7f --- /dev/null +++ b/CARTPOLE/algo_compare.py @@ -0,0 +1,97 @@ +import matplotlib.pyplot as plt +from numpy import loadtxt +import argparse + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--a2c", "-2", help="Takes data from A2C agents", action="store_true") + parser.add_argument("--a3c", "-3", help="Takes data from A3C agents", action="store_true") + parser.add_argument("--a2csync", "-2s", help="Takes data from A2C-Sync agents", action="store_true") + parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") + parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") + parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + global args + args = parser.parse_args() + return args + +def plotter_ep_rew_all(scores, label): + plt.plot(scores, label=label) + plt.axhline(y=200.00, color='r') + plt.ylim(0,500) + plt.ylabel('Reward per Episode') + plt.xlabel('Episode') + + +if __name__ == "__main__": + + args = handleArguments() + + if args.a2c: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + + if args.a3c: + # load array + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.separatedNN: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + + plt.legend() + plt.show() \ No newline at end of file diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index 34ff032..540fc70 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -99,7 +99,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done with global_ep_r.get_lock(): global_ep_r.value = ep_r print(name, "Ep:", global_ep.value, "| Epsidode Reward: %.0f" % global_ep_r.value, "| Duration:", - round(time_done, 5)) + time_done) res_queue.put(global_ep_r.value) time_queue.put(time_done) action_queue.put(a) @@ -126,7 +126,7 @@ def plotter_ep_time_norm(ax1, duration_episode): def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.04) + ax1.set_ylim(0,0.0001) ax1.set_ylabel('Duration of Episode') @@ -171,6 +171,7 @@ def handleArguments(): parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") parser.add_argument("--normalized_plot", "-n", help="Shows plot of normalized reward", action="store_true") + parser.add_argument("--save_data", "-s", help="Saves data for algo_compare.py", action="store_true") global args args = parser.parse_args() return args \ No newline at end of file From 8065952924ebe94cf8895fbc5da99efe99b81faa Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 14 Apr 2020 23:00:12 +0200 Subject: [PATCH 38/63] CARTPOLE - correct coding mistakes with save_data --- CARTPOLE/a2c_cart.py | 9 ++++----- CARTPOLE/a2c_cart_comb.py | 6 +++--- CARTPOLE/a2c_sync_cart.py | 8 ++++---- CARTPOLE/a2c_sync_cart_comb.py | 6 +++--- CARTPOLE/a3c_cart.py | 6 +++--- CARTPOLE/a3c_cart_comb.py | 6 +++--- 6 files changed, 20 insertions(+), 21 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index 73990ab..2ee6879 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -64,8 +64,6 @@ def choose_action(self, s): def loss_func(self, s, a, v_t): self.train() logits, values = self.forward(s) - print ("V_T:", v_t) - print ("Values:", values) td = v_t - values c_loss = td.pow(2) @@ -90,7 +88,7 @@ def loss_func(self, s, a, v_t): scores = [] actions = [] - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 @@ -152,6 +150,7 @@ def loss_func(self, s, a, v_t): "| Normalized Duration:", round(global_time_done, 5)) else: global_ep_r = ep_r + global_time_done = duration print(name, "Ep:", global_ep, "| Epsisode Reward: %.0f" % global_ep_r, "| Duration:", round(global_time_done, 5)) @@ -159,10 +158,10 @@ def loss_func(self, s, a, v_t): durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True else: diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index fae83cc..d590b41 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -89,7 +89,7 @@ def loss_func(self, s, a, v_t): scores = [] actions = [] - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 @@ -158,10 +158,10 @@ def loss_func(self, s, a, v_t): durations.append(duration) scores.append(int(global_ep_r)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and global_ep >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and global_ep >= 10: stop_processes = True else: diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index 85f53df..b974e64 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -20,7 +20,7 @@ os.environ["OMP_NUM_THREADS"] = "1" GAMMA = 0.9 -MAX_EP = 100 +MAX_EP = 2000 env = gym.make('CartPole-v0').unwrapped N_S = env.observation_space.shape[0] @@ -120,10 +120,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -145,7 +145,7 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 9e2c7fa..5d556c1 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -120,10 +120,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -145,7 +145,7 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index 9826a51..6930ed6 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -119,10 +119,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -144,7 +144,7 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 975395e..6d5b0f8 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -118,10 +118,10 @@ def run(self): self.action_queue, self.name) scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot and not handleArguments().save_data: + if handleArguments().load_model and handleArguments().normalized_plot: if np.mean(scores[-min(100, len(scores)):]) >= 400 and self.g_ep.value >= 100: stop_processes = True - elif handleArguments().normalized_plot and not handleArguments().save_data: + elif handleArguments().normalized_plot: if np.mean(scores[-min(10, len(scores)):]) >= 400 and self.g_ep.value >= mp.cpu_count(): stop_processes = True else: @@ -143,7 +143,7 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 From aed3bb4fd6bf6d49f49c2371f7b5b8173f474256 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Tue, 14 Apr 2020 23:30:15 +0200 Subject: [PATCH 39/63] CARTPOLE - separate training and test data --- CARTPOLE/a2c_cart.py | 10 +- CARTPOLE/a2c_cart_comb.py | 20 ++-- CARTPOLE/a2c_sync_cart.py | 9 +- CARTPOLE/a2c_sync_cart_comb.py | 9 +- CARTPOLE/a3c_cart.py | 9 +- CARTPOLE/a3c_cart_comb.py | 9 +- CARTPOLE/algo_compare.py | 194 ++++++++++++++++++++++----------- CARTPOLE/cart_utils.py | 4 +- 8 files changed, 172 insertions(+), 92 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index 2ee6879..74d5920 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -207,9 +207,11 @@ def loss_func(self, s, a, v_t): plt.show() if handleArguments().save_data: - scores = np.asarray([scores]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', scores, delimiter=',') - - + if handleArguments().load_model: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', scores, delimiter=',') + else: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', scores, delimiter=',') sys.exit() diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index d590b41..4b2bd6d 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -31,21 +31,19 @@ def __init__(self, s_dim, a_dim): super(Net, self).__init__() self.s_dim = s_dim self.a_dim = a_dim - self.pi1 = nn.Linear(s_dim, 24) - self.pi12 = nn.Linear(24, 48) - self.pi2 = nn.Linear(48, 24) - self.pi3 = nn.Linear(24, a_dim) - self.v2 = nn.Linear(48, 24) - self.v3 = nn.Linear(24, 1) - set_init([self.pi1, self.pi12, self.pi2, self.pi3, self.v2, self.v3]) + self.pi1 = nn.Linear(s_dim, 80) + self.pi2 = nn.Linear(80, 60) + self.pi3 = nn.Linear(60, a_dim) + self.v2 = nn.Linear(80, 60) + self.v3 = nn.Linear(60, 1) + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical def forward(self, x): pi1 = F.relu(self.pi1(x)) - pi12 = F.relu(self.pi12(pi1)) - pi2 = F.relu(self.pi2(pi12)) + pi2 = F.relu(self.pi2(pi1)) logits = self.pi3(pi2) - v2 = F.relu(self.v2(pi12)) + v2 = F.relu(self.v2(pi1)) values = self.v3(v2) return logits, values @@ -203,7 +201,7 @@ def loss_func(self, s, a, v_t): 'size': 8 } plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Cartpole", fontsize=16) + plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize=16) plt.show() if handleArguments().save_data: diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index b974e64..e68b826 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -233,9 +233,12 @@ def run(self): plt.show() if handleArguments().save_data: - scores = np.asarray([res]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', res, delimiter=',') - + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', scores, delimiter=',') sys.exit() diff --git a/CARTPOLE/a2c_sync_cart_comb.py b/CARTPOLE/a2c_sync_cart_comb.py index 5d556c1..50aca97 100644 --- a/CARTPOLE/a2c_sync_cart_comb.py +++ b/CARTPOLE/a2c_sync_cart_comb.py @@ -224,9 +224,12 @@ def run(self): plotter_ep_rew(ax2, res) if handleArguments().save_data: - scores = np.asarray([res]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', scores, delimiter=',') - + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', scores, delimiter=',') font = {'family': 'serif', 'color': 'darkred', diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index 6930ed6..f722452 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -223,9 +223,12 @@ def run(self): plotter_ep_rew(ax2, res) if handleArguments().save_data: - scores = np.asarray([res]) - np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', scores, delimiter=',') - + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', scores, delimiter=',') font = {'family': 'serif', 'color': 'darkred', diff --git a/CARTPOLE/a3c_cart_comb.py b/CARTPOLE/a3c_cart_comb.py index 6d5b0f8..69e8a3a 100644 --- a/CARTPOLE/a3c_cart_comb.py +++ b/CARTPOLE/a3c_cart_comb.py @@ -222,9 +222,12 @@ def run(self): plotter_ep_rew(ax2, res) if handleArguments().save_data: - scores = np.asarray([res]) - np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', scores, delimiter=',') - + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', scores, delimiter=',') font = {'family': 'serif', 'color': 'darkred', diff --git a/CARTPOLE/algo_compare.py b/CARTPOLE/algo_compare.py index 7769d7f..3071ace 100644 --- a/CARTPOLE/algo_compare.py +++ b/CARTPOLE/algo_compare.py @@ -12,6 +12,7 @@ def handleArguments(): parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--test", "-t", help="Takes data from all agents", action="store_true") global args args = parser.parse_args() return args @@ -28,69 +29,136 @@ def plotter_ep_rew_all(scores, label): args = handleArguments() - if args.a2c: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - - if args.a3c: - # load array - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - - if args.a2csync: - # load array - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.separatedNN: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - if args.sharedNN: - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.all: - # load array - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + if args.test: + if args.a2c: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + + if args.a3c: + # load array + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.separatedNN: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + else: + if args.a2c: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + + if args.a3c: + # load array + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.separatedNN: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + # load array + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + plt.legend() diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index 540fc70..74b6122 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -120,13 +120,13 @@ def plotter_ep_rew(ax2, scores): def plotter_ep_time_norm(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.02) + ax1.set_ylim(0,0.0005) ax1.set_ylabel('Duration of Episode') def plotter_ep_time(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.0001) + ax1.set_ylim(0,0.02) ax1.set_ylabel('Duration of Episode') From 05b2b149852b68ba0777a5beb01a365929687d97 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Thu, 16 Apr 2020 20:51:36 +0200 Subject: [PATCH 40/63] CARTPOLE - remove time stamp in plots --- CARTPOLE/a2c_cart.py | 30 ++++++++++++++++-------------- CARTPOLE/a2c_cart_comb.py | 33 ++++++++++++++++++++------------- CARTPOLE/a2c_sync_cart.py | 2 +- CARTPOLE/cart_utils.py | 2 +- 4 files changed, 38 insertions(+), 29 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index 74d5920..bf015df 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -83,17 +83,17 @@ def loss_func(self, s, a, v_t): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - # Global variables for episodes - durations = [] - scores = [] - actions = [] - if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 for i in range(runs): + # Global variables for episodes + durations = [] + scores = [] + actions = [] + starttime = datetime.now() # load global network @@ -155,7 +155,7 @@ def loss_func(self, s, a, v_t): "| Duration:", round(global_time_done, 5)) - durations.append(duration) + durations.append(global_time_done) scores.append(int(global_ep_r)) if handleArguments().load_model and handleArguments().normalized_plot: @@ -197,21 +197,23 @@ def loss_func(self, s, a, v_t): else: plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', scores, delimiter=',') + else: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', scores, delimiter=',') + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } - plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + #plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Cartpole", fontsize = 16) plt.show() - if handleArguments().save_data: - if handleArguments().load_model: - scores = np.asarray([scores]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', scores, delimiter=',') - else: - scores = np.asarray([scores]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', scores, delimiter=',') sys.exit() diff --git a/CARTPOLE/a2c_cart_comb.py b/CARTPOLE/a2c_cart_comb.py index 4b2bd6d..c5fc034 100644 --- a/CARTPOLE/a2c_cart_comb.py +++ b/CARTPOLE/a2c_cart_comb.py @@ -82,10 +82,7 @@ def loss_func(self, s, a, v_t): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - # Global variables for episodes - durations = [] - scores = [] - actions = [] + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 @@ -95,10 +92,15 @@ def loss_func(self, s, a, v_t): for i in range(runs): starttime = datetime.now() + # Global variables for episodes + durations = [] + scores = [] + actions = [] + # load global network if handleArguments().load_model: gnet = Net(N_S, N_A) - gnet = torch.load("./CARTPOLE/cart_save_model/a2c_cart.pt") + gnet = torch.load("./CARTPOLE/cart_save_model/a2c_cart_comb.pt") gnet.eval() else: gnet = Net(N_S, N_A) @@ -149,11 +151,12 @@ def loss_func(self, s, a, v_t): "| Normalized Duration:", round(global_time_done, 5)) else: global_ep_r = ep_r + global_time_done = duration print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, "| Duration:", round(global_time_done, 5)) - durations.append(duration) + durations.append(global_time_done) scores.append(int(global_ep_r)) if handleArguments().load_model and handleArguments().normalized_plot: @@ -171,7 +174,7 @@ def loss_func(self, s, a, v_t): if np.mean(scores[-min(10, len(scores)):]) >= 300 and not handleArguments().load_model: print("Save model") - torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart.pt") + torch.save(gnet, "./CARTPOLE/cart_save_model/a2c_cart_comb.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -195,18 +198,22 @@ def loss_func(self, s, a, v_t): else: plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', scores, delimiter=',') + else: + scores = np.asarray([scores]) + np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', scores, delimiter=',') + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', 'size': 8 } - plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole (shared NN)", fontsize=16) plt.show() - if handleArguments().save_data: - scores = np.asarray([scores]) - np.savetxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', scores, delimiter=',') - - sys.exit() diff --git a/CARTPOLE/a2c_sync_cart.py b/CARTPOLE/a2c_sync_cart.py index e68b826..75f0b06 100644 --- a/CARTPOLE/a2c_sync_cart.py +++ b/CARTPOLE/a2c_sync_cart.py @@ -228,7 +228,7 @@ def run(self): 'weight': 'normal', 'size': 8, } - plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) + #plt.text(0, 450, f"Average Duration: {timedelta_sum}", fontdict=font) plt.title("Synchronous A2C-Cartpole", fontsize = 16) plt.show() diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index 74b6122..b623f3a 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -120,7 +120,7 @@ def plotter_ep_rew(ax2, scores): def plotter_ep_time_norm(ax1, duration_episode): ax1.plot(duration_episode) - ax1.set_ylim(0,0.0005) + ax1.set_ylim(0,0.01) ax1.set_ylabel('Duration of Episode') From 6fa38cb7e9ef8f39c79fe4af036a70ca29eb8236 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Thu, 16 Apr 2020 20:54:00 +0200 Subject: [PATCH 41/63] CARTPOLE - add new plots, add plot data, add newly trained models --- .../A2C Performance Summary | 11 ----------- .../A2C-Sync Performance Summary | 5 ----- .../A2C_Cart/A2C_cart__shared_train.png | Bin 52833 -> 0 bytes .../A2C_Cart/A2C_cart_shared_test.png | Bin 54786 -> 0 bytes .../A2C_Cart/A2C_cart_test.png | Bin 38018 -> 0 bytes .../A2C_Cart/A2C_cart_train.png | Bin 54174 -> 0 bytes .../Presentable_Results/A2C_Cart/a2c_cart.png | Bin 0 -> 41087 bytes .../A2C_Cart/a2c_cart_comb.png | Bin 0 -> 43796 bytes .../A2C_Cart/a2c_cart_comb_raw.png | Bin 0 -> 56639 bytes .../A2C_Cart/a2c_cart_comb_test.png | Bin 0 -> 34294 bytes .../A2C_Cart/a2c_cart_comb_test_raw.png | Bin 0 -> 46106 bytes .../A2C_Cart/a2c_cart_raw.png | Bin 0 -> 38681 bytes .../A2C_Cart/a2c_cart_test.png | Bin 0 -> 29438 bytes .../A2C_Cart/a2c_cart_test_raw.png | Bin 0 -> 45378 bytes .../A2C_cart_sync_shared_test.png | Bin 39810 -> 36864 bytes .../A2C_cart_sync_shared_test_raw.png | Bin 0 -> 49580 bytes .../A2C_cart_sync_shared_train.png | Bin 40908 -> 36772 bytes .../A2C_cart_sync_shared_train_raw.png | Bin 0 -> 52366 bytes .../A2C_Sync_Cart/A2C_cart_sync_test.png | Bin 41116 -> 34495 bytes .../A2C_Sync_Cart/A2C_cart_sync_test_raw.png | Bin 0 -> 46297 bytes .../A2C_Sync_Cart/A2C_cart_sync_train.png | Bin 56798 -> 35020 bytes .../A2C_Sync_Cart/A2C_cart_sync_train2.png | Bin 38975 -> 0 bytes .../A2C_Sync_Cart/A2C_cart_sync_train_raw.png | Bin 0 -> 44684 bytes .../A3C Performance Summary | 6 ------ .../A3C_Cart/A3C_cart_shared.png | Bin 56307 -> 34251 bytes .../A3C_Cart/A3C_cart_shared_raw.png | Bin 0 -> 44913 bytes .../A3C_Cart/A3C_cart_shared_test.png | Bin 41649 -> 37361 bytes .../A3C_Cart/A3C_cart_shared_test_raw.png | Bin 0 -> 48314 bytes .../A3C_Cart/A3C_cart_test.png | Bin 35292 -> 33480 bytes .../A3C_Cart/A3C_cart_test_raw.png | Bin 0 -> 31424 bytes .../A3C_Cart/A3C_cart_train.png | Bin 47516 -> 35333 bytes .../A3C_Cart/A3C_cart_train_raw.png | Bin 0 -> 40119 bytes .../Compared results/A2C.png | Bin 0 -> 25627 bytes .../Compared results/A2C_test.png | Bin 0 -> 15613 bytes .../Compared results/A3C.png | Bin 0 -> 28805 bytes .../Compared results/All.png | Bin 0 -> 49080 bytes .../Compared results/All_test.png | Bin 0 -> 44427 bytes .../Compared results/a2c_sync.png | Bin 0 -> 25207 bytes .../Compared results/a2c_sync_test.png | Bin 0 -> 26692 bytes .../Compared results/a3c_test.png | Bin 0 -> 28486 bytes .../Compared results/sepNN.png | Bin 0 -> 29688 bytes .../Compared results/sepNN_test.png | Bin 0 -> 24278 bytes .../Compared results/sharedNN.png | Bin 0 -> 36276 bytes .../Compared results/sharedNN_test.png | Bin 0 -> 44339 bytes CARTPOLE/cart_save_model/a2c_cart.pt | Bin 0 -> 12980 bytes CARTPOLE/cart_save_model/a2c_cart_comb.pt | Bin 21893 -> 47740 bytes CARTPOLE/cart_save_model/a2c_sync_cart.pt | Bin 12985 -> 12985 bytes .../cart_save_model/a2c_sync_cart_comb.pt | Bin 26862 -> 47745 bytes CARTPOLE/cart_save_model/a3c_cart.pt | Bin 12988 -> 12988 bytes CARTPOLE/cart_save_model/a3c_cart_comb.pt | Bin 17168 -> 47747 bytes CARTPOLE/cart_save_plot_data/a2c_cart.csv | 1 + .../cart_save_plot_data/a2c_cart_comb.csv | 1 + .../a2c_cart_comb_test.csv | 1 + .../cart_save_plot_data/a2c_cart_sync.csv | 1 + .../cart_save_plot_data/a2c_cart_test.csv | 1 + .../cart_save_plot_data/a2c_sync_cart.csv | 1 + .../a2c_sync_cart_comb.csv | 1 + .../a2c_sync_cart_comb_test.csv | 1 + .../a2c_sync_cart_test.csv | 1 + CARTPOLE/cart_save_plot_data/a3c_cart.csv | 1 + .../cart_save_plot_data/a3c_cart_comb.csv | 1 + .../a3c_cart_comb_test.csv | 1 + .../cart_save_plot_data/a3c_cart_test.csv | 1 + CARTPOLE/cart_save_plot_data/data.csv | 1 + 64 files changed, 14 insertions(+), 22 deletions(-) delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart__shared_train.png delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_shared_test.png delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_test.png delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/A2C_cart_train.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_comb.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_comb_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_comb_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_comb_test_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Cart/a2c_cart_test_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_test_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_shared_train_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_test_raw.png delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train2.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A2C_Sync_Cart/A2C_cart_sync_train_raw.png delete mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C Performance Summary create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_shared_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_shared_test_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_test_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/A3C_Cart/A3C_cart_train_raw.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/A2C.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/A2C_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/A3C.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/All.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/All_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/a2c_sync.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/a2c_sync_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/a3c_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/sepNN.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/sepNN_test.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/sharedNN.png create mode 100644 CARTPOLE/cart_results/Presentable_Results/Compared results/sharedNN_test.png create mode 100644 CARTPOLE/cart_save_model/a2c_cart.pt create mode 100644 CARTPOLE/cart_save_plot_data/a2c_cart.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_cart_sync.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_cart_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/a3c_cart.csv create mode 100644 CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv create mode 100644 CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/a3c_cart_test.csv create mode 100644 CARTPOLE/cart_save_plot_data/data.csv diff --git a/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary b/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary deleted file mode 100644 index 04f552f..0000000 --- a/CARTPOLE/cart_results/Presentable_Results/A2C Performance Summary +++ /dev/null @@ -1,11 +0,0 @@ -A2C Cartpole Performance: - -- Most stable algorithm with episode length of about 0.005 to 0.01 seconds -- Confidence of 95% to have action "right" between 40% and 60% in average, tendency to right as 43%-63% up to 47%-67% also appeared -- Test run performs stable above average reward of 500 -- Breaches stability threshold at about episode 300 to 600 - -Shared NN: - -- Slighty weaker as clearing comes after 1500 episodes (doesn't always clear within 3000 episodes) -- Breaches stability threshold after 500-1000 episodes diff --git a/CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary b/CARTPOLE/cart_results/Presentable_Results/A2C-Sync Performance Summary deleted file mode 100644 index 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diff --git a/CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv b/CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv new file mode 100644 index 0000000..31fc574 --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv @@ -0,0 +1 @@ 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diff --git a/CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv b/CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv new file mode 100644 index 0000000..2f7c37c --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv @@ -0,0 +1 @@ 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diff --git a/CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv b/CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv new file mode 100644 index 0000000..2697603 --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv @@ -0,0 +1 @@ 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diff --git a/CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv b/CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv new file mode 100644 index 0000000..23cdedc --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv @@ -0,0 +1 @@ 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diff --git a/CARTPOLE/cart_save_plot_data/a3c_cart_test.csv b/CARTPOLE/cart_save_plot_data/a3c_cart_test.csv new file mode 100644 index 0000000..a10bb38 --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/a3c_cart_test.csv @@ -0,0 +1 @@ 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diff --git a/CARTPOLE/cart_save_plot_data/data.csv b/CARTPOLE/cart_save_plot_data/data.csv new file mode 100644 index 0000000..3c92d65 --- /dev/null +++ b/CARTPOLE/cart_save_plot_data/data.csv @@ -0,0 +1 @@ +0.000000000000000000e+00,1.000000000000000000e+00,2.000000000000000000e+00,3.000000000000000000e+00,4.000000000000000000e+00,5.000000000000000000e+00,6.000000000000000000e+00,7.000000000000000000e+00,8.000000000000000000e+00,9.000000000000000000e+00 From 534a9300b450cc3a31b0349717d5de1ce8054858 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 17 Apr 2020 09:04:13 +0200 Subject: [PATCH 42/63] VIZDOOM - save_data for A3C, create algo_compare for doom --- VIZDOOM/algo_compare.py | 165 ++++++++++++++++++++++++++++++++++++++++ VIZDOOM/viz_utils.py | 6 +- VIZDOOM/vizdoom_a3c.py | 18 +++-- 3 files changed, 182 insertions(+), 7 deletions(-) create mode 100644 VIZDOOM/algo_compare.py diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py new file mode 100644 index 0000000..4f1984a --- /dev/null +++ b/VIZDOOM/algo_compare.py @@ -0,0 +1,165 @@ +import matplotlib.pyplot as plt +from numpy import loadtxt +import argparse + +def handleArguments(): + """Handles CLI arguments and saves them globally""" + parser = argparse.ArgumentParser( + description="Switch between modes in A2C or loading models from previous games") + parser.add_argument("--a2c", "-2", help="Takes data from A2C agents", action="store_true") + parser.add_argument("--a3c", "-3", help="Takes data from A3C agents", action="store_true") + parser.add_argument("--a2csync", "-S", help="Takes data from A2C-Sync agents", action="store_true") + parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") + parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") + parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--test", "-t", help="Takes data from all agents", action="store_true") + global args + args = parser.parse_args() + return args + +def plotter_ep_rew_all(scores, label): + plt.plot(scores, label=label) + #plt.axhline(y=200.00, color='r') + plt.ylim(0,300) + plt.ylabel('Reward per Episode') + plt.xlabel('Episode') + + +if __name__ == "__main__": + + args = handleArguments() + + if args.test: + if args.a2c: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + + if args.a3c: + # load array + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.separatedNN: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + else: + if args.a2c: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + + if args.a3c: + # load array + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.separatedNN: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_doom.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/cart_save_plot_data/a3c_doom.csv', delimiter=',') + a2c_sync_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_sync_doom.csv', delimiter=',') + + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') + + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + + + plt.legend() + plt.show() \ No newline at end of file diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 01e82d2..2caa6e1 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -86,12 +86,13 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done else: global_ep_r.value = global_ep_r.value * 0.99 + ep_r * 0.01 time_done = global_time_done.value * 0.99 + time_done * 0.01 - print(name, "Ep:", global_ep.value, "| Normalized Reward: %.0f" % global_ep_r.value, "| Normalized Duration:", round(time_done, 5)) + print(name, "Ep:", global_ep.value, "| Normalized Reward: %.0f" % global_ep_r.value, "| Normalized Duration:", + round(time_done, 5)) else: with global_ep_r.get_lock(): global_ep_r.value = ep_r print(name, "Ep:", global_ep.value, "| Epsidode Reward: %.0f" % global_ep_r.value, "| Duration:", - round(time_done, 5)) + time_done) res_queue.put(global_ep_r.value) time_queue.put(time_done) action_queue.put(a) @@ -164,6 +165,7 @@ def handleArguments(): parser.add_argument("--demo_mode", "-d", help="Renders the gym environment", action="store_true") parser.add_argument("--load_model", "-l", help="Loads the model of previously gained training data", action="store_true") parser.add_argument("--normalized_plot", "-n", help="Shows plot of normalized reward", action="store_true") + parser.add_argument("--save_data", "-s", help="Saves data for algo_compare.py", action="store_true") global args args = parser.parse_args() return args diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 3286795..cf1f5ad 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -203,7 +203,7 @@ def run(self): timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 @@ -214,7 +214,7 @@ def run(self): # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./VIZDOOM/doom_save_model/a3c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a2c_doom.pt") model.eval() else: model = Net(len(actions)) @@ -253,7 +253,7 @@ def run(self): if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") + torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom.pt") elif handleArguments().load_model: print("Testing! No need to save model.") else: @@ -279,6 +279,14 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', scores, delimiter=',') + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -286,8 +294,8 @@ def run(self): } if handleArguments().normalized_plot: plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) - else: - plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) + #else: + # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom", fontsize=16) plt.show() From f98f2e1d4ca4d1b06e6bf3ea5d1756bf67ae8859 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sat, 18 Apr 2020 12:09:23 +0200 Subject: [PATCH 43/63] VIZDOOM - new synchronous implementation, reinitialize threads and save stats everytime, create shared NN algorithms --- VIZDOOM/vizdoom_a2c.py | 36 ++- VIZDOOM/vizdoom_a2c_comb.py | 269 ++++++++++++++++++++++ VIZDOOM/vizdoom_a2c_sync.py | 166 ++++++++++---- VIZDOOM/vizdoom_a2c_sync_comb.py | 372 +++++++++++++++++++++++++++++++ VIZDOOM/vizdoom_a3c.py | 4 +- VIZDOOM/vizdoom_a3c_comb.py | 300 +++++++++++++++++++++++++ 6 files changed, 1079 insertions(+), 68 deletions(-) create mode 100644 VIZDOOM/vizdoom_a2c_comb.py create mode 100644 VIZDOOM/vizdoom_a2c_sync_comb.py create mode 100644 VIZDOOM/vizdoom_a3c_comb.py diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 4b42e95..5d29ece 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -68,8 +68,6 @@ def __init__(self, a_dim): self.v2 = nn.Linear(120, 360) self.v3 = nn.Linear(360, 1) - #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -135,7 +133,7 @@ def loss_func(self, s, a, v_t): fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) action = [] - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 @@ -216,26 +214,18 @@ def loss_func(self, s, a, v_t): scores.append(int(global_ep_r)) durations.append(global_time_done) - #if handleArguments().load_model and handleArguments().normalized_plot: - # if np.mean(scores[-min(100, len(scores)):]) >= 50 and global_ep >= 100: - # stop_processes = True - #elif handleArguments().normalized_plot: - # if np.mean(scores[-min(10, len(scores)):]) >= 50 and global_ep >= 10: - # stop_processes = True - #else: - # stop_processes = False break state = s_ total_step += 1 - #if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") - #elif handleArguments().load_model: - # print ("Testing! No need to save model.") - #else: - # print("Failed to train agent. Model was not saved") + if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime @@ -256,6 +246,14 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([scores]) + np.savetxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', scores, delimiter=',') + else: + scores = np.asarray([scores]) + np.savetxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', scores, delimiter=',') + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -263,8 +261,6 @@ def loss_func(self, s, a, v_t): } if handleArguments().normalized_plot: plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) - else: - plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Vizdoom", fontsize = 16) plt.show() diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py new file mode 100644 index 0000000..2f10989 --- /dev/null +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -0,0 +1,269 @@ +import itertools +import numpy as np +from skimage.transform import resize +import time +from datetime import datetime +import matplotlib.pyplot as plt +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from shared_adam import SharedAdam +import os +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_MODEL = 5 +GAMMA = 0.9 +MAX_EP = 2000 +frame_repeat = 12 +resolution = (30, 45) +config_file_path = "VIZDOOM/deadly_corridor.cfg" + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + if handleArguments().demo_mode: + game.set_window_visible(True) + else: + game.set_window_visible(False) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + + +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + +print("Current State:" , state, "\n") +print("Statesize:" , statesize, "\n") +print ("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) + + +class Net(nn.Module): + def __init__(self, a_dim): + super(Net, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 240) + self.pi2 = nn.Linear(240, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v2 = nn.Linear(240, 360) + self.v3 = nn.Linear(360, 1) + + #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) + + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + +if __name__ == '__main__': + + print ("Starting A2C Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) + + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + action = [] + + if handleArguments().normalized_plot and not handleArguments().save_data: + runs = 3 + else: + runs = 1 + + for i in range(runs): + starttime = datetime.now() + + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./VIZDOOM/doom_save_model/a2c_doom_comb.pt") + model.eval() + else: + model = Net(len(actions)) + + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) # global optimizer + + # Global variables for episodes + durations = [] + scores = [] + global_ep, global_ep_r, global_time_done = 1, 0., 0. + name = 'w00' + total_step = 1 + stop_processes = False + + while global_ep < MAX_EP and stop_processes is False: + game.new_episode() + state = game_state(game) + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + + # Initialize stopwatch for average episode duration + start = time.time() + done = False + + a = model.choose_action(state) + action.append(a) + r = game.make_action(actions[a], frame_repeat) + + if game.is_episode_finished(): + done = True + else: + s_ = game_state(game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or total_step % UPDATE_MODEL == 0: # update network + # sync + optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + #print("Acion_array:", action) + print("Total Reward:", game.get_total_reward()) + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: + end = time.time() + duration = end - start + + global_ep += 1 + if handleArguments().normalized_plot: + if global_ep_r == 0.: + global_ep_r = ep_r + else: + global_ep_r = global_ep_r * 0.99 + ep_r * 0.01 + global_time_done = global_time_done * 0.99 + duration * 0.01 + print(name, "Ep:", global_ep, "| Normalized Reward: %.0f" % global_ep_r, + "| Normalized Duration:", round(global_time_done, 5)) + else: + global_ep_r = ep_r + global_time_done = duration + print(name, "Ep:", global_ep, "| Epsidode Reward: %.0f" % global_ep_r, + "| Duration:", + round(global_time_done, 5)) + + scores.append(int(global_ep_r)) + durations.append(global_time_done) + + break + + state = s_ + total_step += 1 + + if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom_comb.pt") + elif handleArguments().load_model: + print ("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) + timedelta_sum += timedelta/3 + + # Get results for confidence intervall + #if handleArguments().load_model: + # confidence_intervall(action, True) + #else: + # confidence_intervall(action) + + # Plot results + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, scores) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, scores) + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([scores]) + np.savetxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', scores, delimiter=',') + else: + scores = np.asarray([scores]) + np.savetxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', scores, delimiter=',') + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + if handleArguments().normalized_plot: + plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Vizdoom (shared NN)", fontsize = 16) + plt.show() + + game.close() + + diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index bcdcc66..edc422f 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -10,7 +10,7 @@ from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -68,8 +68,6 @@ def __init__(self, a_dim): self.v2 = nn.Linear(120, 360) self.v3 = nn.Linear(360, 1) - #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -126,7 +124,7 @@ def loss_func(self, s, a, v_t): class Worker(mp.Process): - def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): + def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() self.name = 'w%02i' % name self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done @@ -134,17 +132,24 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu self.lnet = Net(len(actions)) # local network self.game = initialize_vizdoom(config_file_path) self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.inner_ep = inner_ep + #self.sleep_max = sleep_max def run(self): total_step = 1 stop_processes = False scores = [] - - while self.g_ep.value < MAX_EP and stop_processes is False: + sleep = 15 + superdone = 0 + cpu_count = [] + while self.inner_ep.value < (mp.cpu_count()*3) and stop_processes is False: self.game.new_episode() state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. + + #print("Outside:", sleep) + print("initialized:", self.name) while True: start = time.time() done = False @@ -164,18 +169,63 @@ def run(self): if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network # sync - push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - time.sleep(5) - game.get_total_reward() - buffer_s, buffer_a, buffer_r = [], [], [] + optimize(opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + + #game.get_total_reward() + #buffer_s, buffer_a, buffer_r = [], [], [] if done: + #print("done with:", self.name) + self.inner_ep.value += 1 + print ("Inner Ep:", self.inner_ep.value) end = time.time() time_done = end - start + #print ("Duration:", time_done) + #print("Whats the time?", sleep) record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, self.action_queue, self.name) - - + #print("hore...") + #push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) + #time.sleep(20) + #print(self.sync.get()) + #print("Pull global model") + #self.lnet.load_state_dict(self.gnet.state_dict()) + #print("here...") + + for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + gp._grad = lp.grad + + + #if self.sync.get() == True: + # print("Agent", self.name, "gets in sync") + # self.agent_count.append(1) + # while len(self.agent_count) != mp.cpu_count(): + # print(len(self.agent_count), self.agent_count) + # time.sleep(15) +# + # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + # gp._grad = lp.grad +# + # print("loading state dict") + # self.lnet.load_state_dict(self.gnet.state_dict()) + # if len(self.agent_count) >= 12: + # self.agent_count = [] + # break + + + #if superdone % 10 == 0: +# + # sleep = 120 - 10 * len(self.sync) + # print(sleep) + # print("Pull global model") + # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + # gp._grad = lp.grad + # opt.step() + # time.sleep(sleep) + # self.lnet.load_state_dict(self.gnet.state_dict()) + # if len(self.sync) == mp.cpu_count(): + # self.sync = [] + # break scores.append(int(self.g_ep_r.value)) if handleArguments().load_model and handleArguments().normalized_plot: @@ -198,14 +248,14 @@ def run(self): if __name__ == '__main__': - print ("Starting A3C Agent for Vizdoom-DeadlyCorridor") + print ("Starting A2C-Sync Agent for Vizdoom-DeadlyCorridor") time.sleep(3) timedelta_sum = datetime.now() timedelta_sum -= timedelta_sum fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) - if handleArguments().normalized_plot: + if handleArguments().normalized_plot and not handleArguments().save_data: runs = 3 else: runs = 1 @@ -216,50 +266,75 @@ def run(self): # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./VIZDOOM/doom_save_model/a3c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a2c_sync_doom.pt") model.eval() else: model = Net(len(actions)) # global optimizer opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) - global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) - res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() - # parallel training - if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] - [w.start() for w in workers] - else: - workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in - range(worker_num)] - [w.start() for w in workers] # record episode-reward and duration-episode to plot res = [] durations = [] action = [] - while True: - r = res_queue.get() - t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - if t is not None: - durations.append(t) - if a is not None: - action.append(a) + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() + loop = 0 + + while global_ep.value < MAX_EP: + loop += 1 + print ("loop: ", loop) + # parallel training + inner_ep = mp.Value('i', 0) + manager = mp.Manager() + sync, agent_count = mp.Queue(), manager.list() + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i, sync) for i in range(1)] + [w.start() for w in workers] else: - break + workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in + range(worker_num)] + [w.start() for w in workers] - [w.join() for w in workers] - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") - elif handleArguments().load_model: - print("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") + while True: + #sync.put(False) + #if global_ep.value % (mp.cpu_count()*3) == 0 and global_ep.value != 0: + # for w in workers: + # sync.put(True) + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + if t is not None: + durations.append(t) + if a is not None: + action.append(a) + else: + break + + + for w in workers: + w.join() + w.terminate() + + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_test{loop}.csv", scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom{loop}.csv", scores, delimiter=',') endtime = datetime.now() timedelta = endtime - starttime @@ -281,6 +356,7 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -290,7 +366,7 @@ def run(self): plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) else: plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Vizdoom", fontsize=16) + plt.title("Synchronous A2C-Vizdoom", fontsize=16) plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a2c_sync_comb.py b/VIZDOOM/vizdoom_a2c_sync_comb.py new file mode 100644 index 0000000..d59c1ef --- /dev/null +++ b/VIZDOOM/vizdoom_a2c_sync_comb.py @@ -0,0 +1,372 @@ +import itertools +import numpy as np +from skimage.transform import resize +import time +from datetime import datetime +import matplotlib.pyplot as plt +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 5 +GAMMA = 0.9 +MAX_EP = 2000 +frame_repeat = 12 +resolution = (30, 45) +config_file_path = "VIZDOOM/deadly_corridor.cfg" +worker_num = int(mp.cpu_count()) # Number of reincarnations for Agent + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + game.set_window_visible(False) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + +print("Current State:", state, "\n") +print("Statesize:", statesize, "\n") + +print("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +print("Number of used CPUs: ", worker_num) + +class Net(nn.Module): + def __init__(self, a_dim): + super(Net, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 240) + self.pi2 = nn.Linear(240, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v2 = nn.Linear(240, 360) + self.v3 = nn.Linear(360, 1) + + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done + self.gnet, self.opt = gnet, opt + self.lnet = Net(len(actions)) # local network + self.game = initialize_vizdoom(config_file_path) + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + self.inner_ep = inner_ep + #self.sleep_max = sleep_max + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + sleep = 15 + superdone = 0 + cpu_count = [] + while self.inner_ep.value < (mp.cpu_count()*3) and stop_processes is False: + self.game.new_episode() + state = game_state(self.game) + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + + #print("Outside:", sleep) + print("initialized:", self.name) + while True: + start = time.time() + done = False + a = self.lnet.choose_action(state) + + r = self.game.make_action(actions[a], frame_repeat) + + if self.game.is_episode_finished(): + done = True + else: + s_ = game_state(self.game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network + # sync + optimize(opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + + #game.get_total_reward() + #buffer_s, buffer_a, buffer_r = [], [], [] + + if done: + #print("done with:", self.name) + self.inner_ep.value += 1 + print ("Inner Ep:", self.inner_ep.value) + end = time.time() + time_done = end - start + #print ("Duration:", time_done) + #print("Whats the time?", sleep) + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) + #print("hore...") + #push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) + #time.sleep(20) + #print(self.sync.get()) + #print("Pull global model") + #self.lnet.load_state_dict(self.gnet.state_dict()) + #print("here...") + + for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + gp._grad = lp.grad + + + #if self.sync.get() == True: + # print("Agent", self.name, "gets in sync") + # self.agent_count.append(1) + # while len(self.agent_count) != mp.cpu_count(): + # print(len(self.agent_count), self.agent_count) + # time.sleep(15) +# + # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + # gp._grad = lp.grad +# + # print("loading state dict") + # self.lnet.load_state_dict(self.gnet.state_dict()) + # if len(self.agent_count) >= 12: + # self.agent_count = [] + # break + + + #if superdone % 10 == 0: +# + # sleep = 120 - 10 * len(self.sync) + # print(sleep) + # print("Pull global model") + # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): + # gp._grad = lp.grad + # opt.step() + # time.sleep(sleep) + # self.lnet.load_state_dict(self.gnet.state_dict()) + # if len(self.sync) == mp.cpu_count(): + # self.sync = [] + # break + scores.append(int(self.g_ep_r.value)) + + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: + stop_processes = True + elif handleArguments().normalized_plot: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + else: + stop_processes = False + break + + state = s_ + total_step += 1 + + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == '__main__': + + print ("Starting A2C-Sync Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) + + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + if handleArguments().normalized_plot and not handleArguments().save_data: + runs = 3 + else: + runs = 1 + + for i in range(runs): + starttime = datetime.now() + + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./VIZDOOM/doom_save_model/a2c_sync_doom_comb.pt") + model.eval() + else: + model = Net(len(actions)) + + # global optimizer + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() + loop = 0 + + while global_ep.value < MAX_EP: + loop += 1 + print ("loop: ", loop) + # parallel training + inner_ep = mp.Value('i', 0) + manager = mp.Manager() + sync, agent_count = mp.Queue(), manager.list() + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i, sync) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in + range(worker_num)] + [w.start() for w in workers] + + + while True: + #sync.put(False) + #if global_ep.value % (mp.cpu_count()*3) == 0 and global_ep.value != 0: + # for w in workers: + # sync.put(True) + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + if t is not None: + durations.append(t) + if a is not None: + action.append(a) + else: + break + + + for w in workers: + w.join() + w.terminate() + + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom_comb.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test{loop}.csv", scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb{loop}.csv", scores, delimiter=',') + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta / 3 + + # Get results for confidence intervall + # if handleArguments().load_model: + # confidence_intervall(action, True) + # else: + # confidence_intervall(action) + + # Plot results + + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + if handleArguments().normalized_plot: + plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + else: + plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Synchronous A2C Vizdoom (shared NN)", fontsize=16) + plt.show() + + game.close() + sys.exit() + diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index cf1f5ad..d0b3fac 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -68,8 +68,6 @@ def __init__(self, a_dim): self.v2 = nn.Linear(120, 360) self.v3 = nn.Linear(360, 1) - #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) - set_init([self.pi1, self.pi2, self.pi3, self.v1, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -214,7 +212,7 @@ def run(self): # load global network if handleArguments().load_model: model = Net(len(actions)) - model = torch.load("./VIZDOOM/doom_save_model/a2c_doom.pt") + model = torch.load("./VIZDOOM/doom_save_model/a3c_doom.pt") model.eval() else: model = Net(len(actions)) diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py new file mode 100644 index 0000000..ed35155 --- /dev/null +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -0,0 +1,300 @@ +import itertools +import numpy as np +from skimage.transform import resize +import time +from datetime import datetime +import matplotlib.pyplot as plt +import torch +from torch import nn +import torch.nn.functional as F +from tqdm import trange +from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution + +from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +import torch.multiprocessing as mp +from shared_adam import SharedAdam +import sys +import os +os.environ["OMP_NUM_THREADS"] = "1" + +UPDATE_GLOBAL_ITER = 5 +GAMMA = 0.9 +MAX_EP = 2000 +frame_repeat = 12 +resolution = (30, 45) +config_file_path = "VIZDOOM/deadly_corridor.cfg" +worker_num = int(mp.cpu_count()) # Number of reincarnations for Agent + + +def initialize_vizdoom(config): + game = DoomGame() + game.load_config(config) + game.set_window_visible(False) + game.set_mode(Mode.PLAYER) + game.set_screen_format(ScreenFormat.GRAY8) + game.set_screen_resolution(ScreenResolution.RES_640X480) + game.init() + return game + +def preprocess(img): + return torch.from_numpy(resize(img, resolution).astype(np.float32)) + + +def game_state(game): + return preprocess(game.get_state().screen_buffer) + +game = initialize_vizdoom(config_file_path) +statesize = (game_state(game).shape[0]) +state = game_state(game) +n = game.get_available_buttons_size() +actions = [list(a) for a in itertools.product([0, 1], repeat=n)] + +print("Current State:", state, "\n") +print("Statesize:", statesize, "\n") + +print("Action Size: ", n) +print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +print("Number of used CPUs: ", worker_num) + +class Net(nn.Module): + def __init__(self, a_dim): + super(Net, self).__init__() + self.s_dim = 45 + self.a_dim = a_dim + self.pi1 = nn.Linear(self.s_dim, 240) + self.pi2 = nn.Linear(240, 360) + self.pi3 = nn.Linear(360, a_dim) + self.v2 = nn.Linear(240, 360) + self.v3 = nn.Linear(360, 1) + + set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) + self.distribution = torch.distributions.Categorical + + def forward(self, x): + pi1 = F.relu(self.pi1(x)) + pi2 = F.relu(self.pi2(pi1)) + logits = self.pi3(pi2) + v2 = F.relu(self.v2(pi1)) + values = self.v3(v2) + return logits, values + + def set_init(layers): + for layer in layers: + nn.init.xavier_uniform_(layer.weight, nn.init.calculate_gain('relu')) + nn.init.xavier_uniform_(layer.bias, nn.init.calculate_gain('relu')) + + def choose_action(self, s): + self.eval() + logits, _ = self.forward(s) + prob = F.softmax(logits, dim=1).data + m = self.distribution(prob) + return m.sample().numpy()[0] + + def loss_func(self, s, a, v_t): + self.train() + logits, values = self.forward(s) + + new_values = torch.zeros([len(v_t), 1], dtype=torch.float32) + # Reshape Tensor of values + for i in range(len(v_t)): + for j in range(30): + values[i][0] += values[i+j][0] + new_values[i][0] = values[i][0] + + td = v_t - new_values + c_loss = td.pow(2) + new_logits = torch.zeros([len(v_t), 128], dtype=torch.float32) + # Reshape Tensor of logits + for i in range(len(logits[0])): + countrow = 0 + for j in range(len(logits)): + logits[countrow][i] += logits[j][i] + if j % 30 == 0: + new_logits[countrow][i] = logits[countrow][i] + countrow += 1 + probs = F.softmax(new_logits, dim=1) + m = self.distribution(probs) + exp_v = m.log_prob(a) * td.detach().squeeze() + a_loss = -exp_v + total_loss = (c_loss + a_loss).mean() + return total_loss + + + +class Worker(mp.Process): + def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): + super(Worker, self).__init__() + self.name = 'w%02i' % name + self.g_ep, self.g_ep_r, self.g_time = global_ep, global_ep_r, global_time_done + self.gnet, self.opt = gnet, opt + self.lnet = Net(len(actions)) # local network + self.game = initialize_vizdoom(config_file_path) + self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue + + def run(self): + total_step = 1 + stop_processes = False + scores = [] + + while self.g_ep.value < MAX_EP and stop_processes is False: + self.game.new_episode() + state = game_state(self.game) + buffer_s, buffer_a, buffer_r = [], [], [] + ep_r = 0. + while True: + start = time.time() + done = False + a = self.lnet.choose_action(state) + + r = self.game.make_action(actions[a], frame_repeat) + + if self.game.is_episode_finished(): + done = True + else: + s_ = game_state(self.game) + + ep_r += r + buffer_a.append(a) + buffer_s.append(state) + buffer_r.append(r) + + if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network + # sync + push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) + game.get_total_reward() + buffer_s, buffer_a, buffer_r = [], [], [] + + if done: + end = time.time() + time_done = end - start + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, + self.action_queue, self.name) + + scores.append(int(self.g_ep_r.value)) + + if handleArguments().load_model and handleArguments().normalized_plot: + if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: + stop_processes = True + elif handleArguments().normalized_plot: + if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): + stop_processes = True + else: + stop_processes = False + break + + state = s_ + total_step += 1 + + self.time_queue.put(None) + self.res_queue.put(None) + self.action_queue.put(None) + + +if __name__ == '__main__': + + print ("Starting A3C Agent for Vizdoom-DeadlyCorridor") + time.sleep(3) + + timedelta_sum = datetime.now() + timedelta_sum -= timedelta_sum + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + if handleArguments().normalized_plot and not handleArguments().save_data: + runs = 3 + else: + runs = 1 + + for i in range(runs): + starttime = datetime.now() + + # load global network + if handleArguments().load_model: + model = Net(len(actions)) + model = torch.load("./VIZDOOM/doom_save_model/a3c_doom_comb.pt") + model.eval() + else: + model = Net(len(actions)) + + # global optimizer + opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) + global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) + res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() + # parallel training + if handleArguments().load_model: + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] + [w.start() for w in workers] + else: + workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in + range(worker_num)] + [w.start() for w in workers] + + # record episode-reward and duration-episode to plot + res = [] + durations = [] + action = [] + while True: + r = res_queue.get() + t = time_queue.get() + a = action_queue.get() + if r is not None: + res.append(r) + if t is not None: + durations.append(t) + if a is not None: + action.append(a) + else: + break + + [w.join() for w in workers] + + if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") + elif handleArguments().load_model: + print("Testing! No need to save model.") + else: + print("Failed to train agent. Model was not saved") + + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + timedelta_sum += timedelta / 3 + + # Get results for confidence intervall + # if handleArguments().load_model: + # confidence_intervall(action, True) + # else: + # confidence_intervall(action) + + # Plot results + + if handleArguments().normalized_plot: + plotter_ep_time_norm(ax1, durations) + plotter_ep_rew_norm(ax2, res) + else: + plotter_ep_time(ax1, durations) + plotter_ep_rew(ax2, res) + + if handleArguments().save_data: + if handleArguments().load_model: + scores = np.asarray([res]) + np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', scores, delimiter=',') + else: + scores = np.asarray([res]) + np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', scores, delimiter=',') + + font = {'family': 'serif', + 'color': 'darkred', + 'weight': 'normal', + 'size': 8 + } + if handleArguments().normalized_plot: + plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + #else: + # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("A3C-Vizdoom (shared NN)", fontsize=16) + plt.show() + + game.close() + sys.exit() + From 439e96487d334d3902ed6a1dcb0af9ddf1b474d7 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sat, 18 Apr 2020 12:13:49 +0200 Subject: [PATCH 44/63] VIZDOOM - refactor viz_utils --- VIZDOOM/viz_utils.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 2caa6e1..8e9b3b8 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -4,6 +4,7 @@ from torch import nn import torch +import torch.multiprocessing as mp import numpy as np import matplotlib.pyplot as plt import pickle @@ -43,11 +44,12 @@ def push_and_pull(opt, lnet, gnet, done, s_, bs, ba, br, gamma): # calculate local gradients and push local parameters to global opt.zero_grad() loss.backward() + + # push and pull global parameters for lp, gp in zip(lnet.parameters(), gnet.parameters()): gp._grad = lp.grad - opt.step() - # pull global parameters + opt.step() lnet.load_state_dict(gnet.state_dict()) @@ -91,7 +93,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done else: with global_ep_r.get_lock(): global_ep_r.value = ep_r - print(name, "Ep:", global_ep.value, "| Epsidode Reward: %.0f" % global_ep_r.value, "| Duration:", + print(name, "Ep:", global_ep.value, "| Episode Reward: %.0f" % global_ep_r.value, "| Duration:", time_done) res_queue.put(global_ep_r.value) time_queue.put(time_done) From 8f2e140751383e7b529f1c0d722fb81fc4993172 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sat, 18 Apr 2020 21:59:59 +0200 Subject: [PATCH 45/63] VIZDOOM - refactor plotter --- VIZDOOM/algo_compare.py | 4 ++-- VIZDOOM/viz_utils.py | 4 ++-- VIZDOOM/vizdoom_a2c.py | 2 +- VIZDOOM/vizdoom_a2c_comb.py | 2 +- VIZDOOM/vizdoom_a2c_sync.py | 18 +++++++++--------- VIZDOOM/vizdoom_a2c_sync_comb.py | 8 ++++---- VIZDOOM/vizdoom_a3c.py | 2 +- VIZDOOM/vizdoom_a3c_comb.py | 14 +++++++------- 8 files changed, 27 insertions(+), 27 deletions(-) diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index 4f1984a..c777f4f 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -19,8 +19,8 @@ def handleArguments(): def plotter_ep_rew_all(scores, label): plt.plot(scores, label=label) - #plt.axhline(y=200.00, color='r') - plt.ylim(0,300) + plt.axhline(y=0, color='r') + plt.ylim(-120,450) plt.ylabel('Reward per Episode') plt.xlabel('Episode') diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 8e9b3b8..a22bbf5 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -102,8 +102,8 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done def plotter_ep_rew_norm(ax2, scores): ax2.plot(scores) - #ax2.axhline(y=200.00, color='r') - ax2.set_ylim(-120,300) + ax2.axhline(y=0, color='r') + ax2.set_ylim(-120,500) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 5d29ece..c4fa96c 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -260,7 +260,7 @@ def loss_func(self, s, a, v_t): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Vizdoom", fontsize = 16) plt.show() diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py index 2f10989..9af442c 100644 --- a/VIZDOOM/vizdoom_a2c_comb.py +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -260,7 +260,7 @@ def loss_func(self, s, a, v_t): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 250, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Vanilla A2C-Vizdoom (shared NN)", fontsize = 16) plt.show() diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index edc422f..ad5ad84 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -280,7 +280,7 @@ def run(self): action = [] global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() - loop = 0 + loop = 20 while global_ep.value < MAX_EP: loop += 1 @@ -320,7 +320,7 @@ def run(self): w.join() w.terminate() - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + if not handleArguments().load_model and global_ep.value >= 10: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom.pt") elif handleArguments().load_model: @@ -336,10 +336,10 @@ def run(self): scores = np.asarray([res]) np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom{loop}.csv", scores, delimiter=',') - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta / 3 + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) + #timedelta_sum += timedelta / 3 # Get results for confidence intervall # if handleArguments().load_model: @@ -363,9 +363,9 @@ def run(self): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) - else: - plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + #else: + # plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Synchronous A2C-Vizdoom", fontsize=16) plt.show() diff --git a/VIZDOOM/vizdoom_a2c_sync_comb.py b/VIZDOOM/vizdoom_a2c_sync_comb.py index d59c1ef..238c42b 100644 --- a/VIZDOOM/vizdoom_a2c_sync_comb.py +++ b/VIZDOOM/vizdoom_a2c_sync_comb.py @@ -318,7 +318,7 @@ def run(self): w.join() w.terminate() - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + if not handleArguments().load_model and global_ep.value >= 10: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom_comb.pt") elif handleArguments().load_model: @@ -361,9 +361,9 @@ def run(self): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) - else: - plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + #else: + # plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("Synchronous A2C Vizdoom (shared NN)", fontsize=16) plt.show() diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index d0b3fac..0b4b989 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -291,7 +291,7 @@ def run(self): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) #else: # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom", fontsize=16) diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py index ed35155..a28d76c 100644 --- a/VIZDOOM/vizdoom_a3c_comb.py +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -247,13 +247,13 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") - elif handleArguments().load_model: + #if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") + if handleArguments().load_model: print("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") + #else: + # print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime @@ -289,7 +289,7 @@ def run(self): 'size': 8 } if handleArguments().normalized_plot: - plt.text(0, 50, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) #else: # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) plt.title("A3C-Vizdoom (shared NN)", fontsize=16) From d71c28a3a39962016b301ca7aff71f3909b86140 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 20 Apr 2020 01:29:41 +0200 Subject: [PATCH 46/63] VIZDOOM - redefine save model condition --- VIZDOOM/viz_utils.py | 13 ++++++------- VIZDOOM/vizdoom_a2c.py | 22 ++++++++++++++-------- VIZDOOM/vizdoom_a2c_comb.py | 20 ++++++++++++-------- VIZDOOM/vizdoom_a3c.py | 23 +++++++++++------------ VIZDOOM/vizdoom_a3c_comb.py | 37 +++++++++++++++++-------------------- 5 files changed, 60 insertions(+), 55 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index a22bbf5..5e407ff 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -109,7 +109,7 @@ def plotter_ep_rew_norm(ax2, scores): def plotter_ep_rew(ax2, scores): ax2.plot(scores) - #ax2.axhline(y=200.00, color='r') + ax2.axhline(y=0, color='r') ax2.set_ylim(-120,550) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') @@ -125,8 +125,7 @@ def plotter_ep_time(ax1, duration_episode): ax1.set_ylabel('Duration of Episode') -# TODO: check for reasonable count length and adjust -def confidence_intervall(actions, load_model = False): +def confidence_intervall(actions, load_model=False): count = 1 probab_count = 0 probabilities = [] @@ -144,19 +143,19 @@ def confidence_intervall(actions, load_model = False): if load_model == True: stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 - print("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", + print("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[0] - stan_dev1, 3), " and", round(probabilities[0] + stan_dev1, 3)) else: stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 - print ("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) + print ("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", + print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", round(probabilities[2] + stan_dev2, 3)) stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", round(probabilities[3] + stan_dev3, 3)) diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index c4fa96c..d46baff 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -56,6 +56,11 @@ def game_state(game): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -171,7 +176,13 @@ def loss_func(self, s, a, v_t): done = False a = model.choose_action(state) - action.append(a) + #print ("Action", a) + for i in range(len(attack)): + if attack[i] == a: + action.append(1) + else: + action.append(0) + r = game.make_action(actions[a], frame_repeat) if game.is_episode_finished(): @@ -187,8 +198,6 @@ def loss_func(self, s, a, v_t): if done or total_step % UPDATE_MODEL == 0: # update network # sync optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - #print("Acion_array:", action) - print("Total Reward:", game.get_total_reward()) buffer_s, buffer_a, buffer_r = [], [], [] if done: @@ -219,7 +228,7 @@ def loss_func(self, s, a, v_t): state = s_ total_step += 1 - if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + if not handleArguments().load_model and global_ep >= 10: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") elif handleArguments().load_model: @@ -233,10 +242,7 @@ def loss_func(self, s, a, v_t): timedelta_sum += timedelta/3 # Get results for confidence intervall - #if handleArguments().load_model: - # confidence_intervall(action, True) - #else: - # confidence_intervall(action) + confidence_intervall(action) # Plot results if handleArguments().normalized_plot: diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py index 9af442c..2d37d45 100644 --- a/VIZDOOM/vizdoom_a2c_comb.py +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -55,6 +55,10 @@ def game_state(game): print ("Action Size: ", n) print("All possible Actions:", actions, "\n", "Total: ", len(actions)) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) class Net(nn.Module): def __init__(self, a_dim): @@ -171,7 +175,12 @@ def loss_func(self, s, a, v_t): done = False a = model.choose_action(state) - action.append(a) + for i in range(len(attack)): + if attack[i] == a: + action.append(1) + else: + action.append(0) + r = game.make_action(actions[a], frame_repeat) if game.is_episode_finished(): @@ -187,8 +196,6 @@ def loss_func(self, s, a, v_t): if done or total_step % UPDATE_MODEL == 0: # update network # sync optimize(opt, model, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - #print("Acion_array:", action) - print("Total Reward:", game.get_total_reward()) buffer_s, buffer_a, buffer_r = [], [], [] if done: @@ -219,7 +226,7 @@ def loss_func(self, s, a, v_t): state = s_ total_step += 1 - if np.mean(scores[-min(10, len(scores)):]) >= 0 and not handleArguments().load_model and global_ep >= 10: + if not handleArguments().load_model and global_ep >= 10: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom_comb.pt") elif handleArguments().load_model: @@ -233,10 +240,7 @@ def loss_func(self, s, a, v_t): timedelta_sum += timedelta/3 # Get results for confidence intervall - #if handleArguments().load_model: - # confidence_intervall(action, True) - #else: - # confidence_intervall(action) + confidence_intervall(action) # Plot results if handleArguments().normalized_plot: diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 0b4b989..59b5e46 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -56,6 +56,11 @@ def game_state(game): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) print("Number of used CPUs: ", worker_num) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -147,6 +152,11 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) + for i in range(len(attack)): + if attack[i] == a: + self.action_queue.put(1) + else: + self.action_queue.put(0) r = self.game.make_action(actions[a], frame_repeat) @@ -174,14 +184,6 @@ def run(self): scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: - stop_processes = True - elif handleArguments().normalized_plot: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - else: - stop_processes = False break state = s_ @@ -189,7 +191,6 @@ def run(self): self.time_queue.put(None) self.res_queue.put(None) - self.action_queue.put(None) if __name__ == '__main__': @@ -249,13 +250,11 @@ def run(self): [w.join() for w in workers] - if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: + if not handleArguments().load_model and global_ep.value >= 10: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom.pt") elif handleArguments().load_model: print("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py index a28d76c..6cf8306 100644 --- a/VIZDOOM/vizdoom_a3c_comb.py +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -56,6 +56,11 @@ def game_state(game): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) print("Number of used CPUs: ", worker_num) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -145,6 +150,11 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) + for i in range(len(attack)): + if attack[i] == a: + action_queue.put(1) + else: + action_queue.put(0) r = self.game.make_action(actions[a], frame_repeat) @@ -161,7 +171,6 @@ def run(self): if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network # sync push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - game.get_total_reward() buffer_s, buffer_a, buffer_r = [], [], [] if done: @@ -172,14 +181,6 @@ def run(self): scores.append(int(self.g_ep_r.value)) - if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: - stop_processes = True - elif handleArguments().normalized_plot: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - else: - stop_processes = False break state = s_ @@ -187,7 +188,6 @@ def run(self): self.time_queue.put(None) self.res_queue.put(None) - self.action_queue.put(None) if __name__ == '__main__': @@ -247,13 +247,11 @@ def run(self): [w.join() for w in workers] - #if np.mean(res[-min(10, len(res)):]) >= 0 and not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") + if not handleArguments().load_model and global_ep.value >= 10: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") if handleArguments().load_model: print("Testing! No need to save model.") - #else: - # print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime @@ -261,13 +259,12 @@ def run(self): timedelta_sum += timedelta / 3 # Get results for confidence intervall - # if handleArguments().load_model: - # confidence_intervall(action, True) - # else: - # confidence_intervall(action) + if handleArguments().load_model: + confidence_intervall(action, True) + else: + confidence_intervall(action) # Plot results - if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) plotter_ep_rew_norm(ax2, res) From 89b3e6d73e26b6aa9669c9008dccc49e368aac05 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 20 Apr 2020 15:48:09 +0200 Subject: [PATCH 47/63] VIZDOOM - refactor action_queue, take action_queue out from viz_utils --- VIZDOOM/viz_utils.py | 3 +-- VIZDOOM/vizdoom_a3c.py | 21 +++++++++++---------- VIZDOOM/vizdoom_a3c_comb.py | 14 +++++--------- 3 files changed, 17 insertions(+), 21 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 5e407ff..784c4d6 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -77,7 +77,7 @@ def optimize(opt, lnet, done, s_, bs, ba, br, gamma): opt.step() -def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done, time_done, a, action_queue, name): +def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done, time_done, name): with global_ep.get_lock(): global_ep.value += 1 @@ -97,7 +97,6 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done time_done) res_queue.put(global_ep_r.value) time_queue.put(time_done) - action_queue.put(a) def plotter_ep_rew_norm(ax2, scores): diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 59b5e46..8a89d4f 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -140,10 +140,9 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu def run(self): total_step = 1 - stop_processes = False scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: + while self.g_ep.value < MAX_EP: self.game.new_episode() state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] @@ -152,12 +151,12 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) - for i in range(len(attack)): - if attack[i] == a: - self.action_queue.put(1) - else: - self.action_queue.put(0) + if a in attack: + self.action_queue.put(1) + else: + self.action_queue.put(0) + r = self.game.make_action(actions[a], frame_repeat) if self.game.is_episode_finished(): @@ -179,9 +178,7 @@ def run(self): if done: end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, - self.action_queue, self.name) - + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, self.name) scores.append(int(self.g_ep_r.value)) break @@ -189,8 +186,11 @@ def run(self): state = s_ total_step += 1 + + print("Out of here") self.time_queue.put(None) self.res_queue.put(None) + self.action_queue.put(None) if __name__ == '__main__': @@ -239,6 +239,7 @@ def run(self): r = res_queue.get() t = time_queue.get() a = action_queue.get() + if r is not None: res.append(r) if t is not None: diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py index 6cf8306..4e6d62d 100644 --- a/VIZDOOM/vizdoom_a3c_comb.py +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -150,11 +150,10 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) - for i in range(len(attack)): - if attack[i] == a: - action_queue.put(1) - else: - action_queue.put(0) + if a in attack: + self.action_queue.put(1) + else: + self.action_queue.put(0) r = self.game.make_action(actions[a], frame_repeat) @@ -176,11 +175,8 @@ def run(self): if done: end = time.time() time_done = end - start - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, - self.action_queue, self.name) - + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, self.name) scores.append(int(self.g_ep_r.value)) - break state = s_ From bd7b79eaea3d9b69d1783665bedea6f801b0d3c5 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 24 Apr 2020 19:49:46 +0200 Subject: [PATCH 48/63] VIZDOOM - set ylim to 1000 in viz_utils --- VIZDOOM/viz_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 784c4d6..12a31f9 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -109,7 +109,7 @@ def plotter_ep_rew_norm(ax2, scores): def plotter_ep_rew(ax2, scores): ax2.plot(scores) ax2.axhline(y=0, color='r') - ax2.set_ylim(-120,550) + ax2.set_ylim(-120,1000) ax2.set_ylabel('Reward per Episode') ax2.set_xlabel('Episode') From 19b5fb4d4b16e545d6749b9c3531ab71e45420ff Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 26 Apr 2020 00:00:44 +0200 Subject: [PATCH 49/63] VIZDOOM - combine CSVs and plot for A2C-sync --- VIZDOOM/algo_compare.py | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index c777f4f..7a4823a 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -1,6 +1,7 @@ import matplotlib.pyplot as plt from numpy import loadtxt import argparse +import numpy as np def handleArguments(): """Handles CLI arguments and saves them globally""" @@ -113,11 +114,24 @@ def plotter_ep_rew_all(scores, label): if args.a2csync: # load array - a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') + a2c_sync_arr = [] + for i in range(1, 44): + a2c_sync_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C/a2c_sync_doom{i}.csv', delimiter=',') + if a2c_sync_data != []: + for j in a2c_sync_data: + a2c_sync_arr.append(j) + + plotter_ep_rew_all(a2c_sync_arr, "A2C-Sync") + + a2c_sync_comb_arr = [] + for j in range(1,43): + a2c_sync_comb_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C_Comb/a2c_sync_doom_comb{j}.csv', delimiter=',') + if a2c_sync_comb_data != []: + for j in a2c_sync_comb_data: + a2c_sync_comb_arr.append(j) + + plotter_ep_rew_all(a2c_sync_comb_arr, "A2C-Sync (shared NN)") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") if args.separatedNN: # load array From fbe26800d0ad46071dc3ff473888f8e6120731ec Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 26 Apr 2020 00:33:21 +0200 Subject: [PATCH 50/63] VIZDOOM - algo_compare with subplots of training and testing data --- VIZDOOM/algo_compare.py | 268 ++++++++++++++++++---------------------- 1 file changed, 118 insertions(+), 150 deletions(-) diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index 7a4823a..7be9f71 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -13,165 +13,133 @@ def handleArguments(): parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") - parser.add_argument("--test", "-t", help="Takes data from all agents", action="store_true") global args args = parser.parse_args() return args -def plotter_ep_rew_all(scores, label): - plt.plot(scores, label=label) - plt.axhline(y=0, color='r') - plt.ylim(-120,450) - plt.ylabel('Reward per Episode') - plt.xlabel('Episode') +def plotter_ep_rew_all(ax, scores, label): + ax.plot(scores, label=label) + ax.axhline(y=0, color='r') + ax.set_ylim(-120,450) + ax.set_ylabel('Reward per Episode') + if __name__ == "__main__": args = handleArguments() - - if args.test: - if args.a2c: - # load array - a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - - if args.a3c: - # load array - a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - - if args.a2csync: - # load array - a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.separatedNN: - # load array - a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') - a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') - a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - if args.sharedNN: - # load array - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.all: - # load array - # load array - a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') - a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') - a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - # load array - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - else: - if args.a2c: - # load array - a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - - if args.a3c: - # load array - a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - - if args.a2csync: - # load array - a2c_sync_arr = [] - for i in range(1, 44): - a2c_sync_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C/a2c_sync_doom{i}.csv', delimiter=',') - if a2c_sync_data != []: - for j in a2c_sync_data: - a2c_sync_arr.append(j) - - plotter_ep_rew_all(a2c_sync_arr, "A2C-Sync") - - a2c_sync_comb_arr = [] - for j in range(1,43): - a2c_sync_comb_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C_Comb/a2c_sync_doom_comb{j}.csv', delimiter=',') - if a2c_sync_comb_data != []: - for j in a2c_sync_comb_data: - a2c_sync_comb_arr.append(j) - - plotter_ep_rew_all(a2c_sync_comb_arr, "A2C-Sync (shared NN)") - - - if args.separatedNN: - # load array - a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') - a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') - a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - if args.sharedNN: - # load array - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.all: - # load array - # load array - a2c_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_doom.csv', delimiter=',') - a3c_data = loadtxt('VIZDOOM/cart_save_plot_data/a3c_doom.csv', delimiter=',') - a2c_sync_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_sync_doom.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - # load array - a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + if args.a2c: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + a2c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') + a2c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax1,a2c_data, "A2C") + plotter_ep_rew_all(ax1, a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(ax2, a2c_data_test, "A2C") + plotter_ep_rew_all(ax2, a2c_comb_data_test, "A2C (shared NN)") + ax2.set_xlabel('Episode') + plt.title("Vanilla A2C-Vizdoom", fontsize=16) + + if args.a3c: + # load array + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + a3c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') + a3c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax1,a3c_data, "A3C") + plotter_ep_rew_all(ax1, a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(ax2, a3c_data_test, "A3C") + plotter_ep_rew_all(ax2, a3c_comb_data_test, "A3C (shared NN)") + ax2.set_xlabel('Episode') + plt.title("A3C-Vizdoom", fontsize=16) + + if args.a2csync: + # load array + a2c_sync_arr = [] + for i in range(1, 44): + a2c_sync_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C/a2c_sync_doom{i}.csv', delimiter=',') + if a2c_sync_data != []: + for j in a2c_sync_data: + a2c_sync_arr.append(j) + plotter_ep_rew_all(ax1, a2c_sync_arr, "A2C-Sync") + a2c_sync_comb_arr = [] + for j in range(1,43): + a2c_sync_comb_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C_Comb/a2c_sync_doom_comb{j}.csv', delimiter=',') + if a2c_sync_comb_data != []: + for j in a2c_sync_comb_data: + a2c_sync_comb_arr.append(j) + plotter_ep_rew_all(ax1, a2c_sync_comb_arr, "A2C-Sync (shared NN)") + a2c_sync_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_sync_data_test, "A2C-Sync") + a2c_sync_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_sync_comb_data_test, "A2C-Sync (shared NN)") + ax2.set_xlabel('Episode') + plt.title("Synchronous A2C-Vizdoom", fontsize=16) + + if args.separatedNN: + # load array + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') + a2c_sync_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom.csv', delimiter=',') + plotter_ep_rew_all(a2c_data, "A2C") + plotter_ep_rew_all(a3c_data, "A3C") + plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") + + if args.sharedNN: + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + a2c_sync_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb.csv', delimiter=',') + plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + + if args.all: + # load array + # load array + a2c_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_doom.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/cart_save_plot_data/a3c_doom.csv', delimiter=',') + a2c_sync_arr = [] + for i in range(1, 44): + a2c_sync_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C/a2c_sync_doom{i}.csv', delimiter=',') + if a2c_sync_data != []: + for j in a2c_sync_data: + a2c_sync_arr.append(j) + plotter_ep_rew_all(ax1, a2c_data, "A2C") + plotter_ep_rew_all(ax1, a3c_data, "A3C") + plotter_ep_rew_all(ax1, a2c_sync_arr, "A2C-Sync") + # load array + a2c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb.csv', delimiter=',') + a3c_comb_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', delimiter=',') + a2c_sync_comb_arr = [] + for j in range(1, 43): + a2c_sync_comb_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C_Comb/a2c_sync_doom_comb{j}.csv', + delimiter=',') + if a2c_sync_comb_data != []: + for j in a2c_sync_comb_data: + a2c_sync_comb_arr.append(j) + plotter_ep_rew_all(ax1, a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(ax1,a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(ax1, a2c_sync_comb_arr, "A2C-Sync (shared NN)") + a2c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') + a3c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') + a2c_sync_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_data_test, "A2C") + plotter_ep_rew_all(ax2, a3c_data_test, "A3C") + plotter_ep_rew_all(ax2, a2c_sync_data_test, "A2C-Sync") + # load array + a2c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') + a3c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') + a2c_sync_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(ax2, a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(ax2, a2c_sync_comb_data_test, "A2C-Sync (shared NN)") + + ax2.set_xlabel('Episode') + plt.title("Advantage Actor-Critic Vizdoom", fontsize=16) From 3c8501a92607af055e352800d5709981e97c68a6 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 26 Apr 2020 01:13:19 +0200 Subject: [PATCH 51/63] CARTPOLE - make subplots for training and testing data --- CARTPOLE/algo_compare.py | 227 +++++++++++++++------------------------ 1 file changed, 87 insertions(+), 140 deletions(-) diff --git a/CARTPOLE/algo_compare.py b/CARTPOLE/algo_compare.py index 3071ace..094823e 100644 --- a/CARTPOLE/algo_compare.py +++ b/CARTPOLE/algo_compare.py @@ -1,6 +1,7 @@ import matplotlib.pyplot as plt from numpy import loadtxt import argparse +import numpy as np def handleArguments(): """Handles CLI arguments and saves them globally""" @@ -8,156 +9,102 @@ def handleArguments(): description="Switch between modes in A2C or loading models from previous games") parser.add_argument("--a2c", "-2", help="Takes data from A2C agents", action="store_true") parser.add_argument("--a3c", "-3", help="Takes data from A3C agents", action="store_true") - parser.add_argument("--a2csync", "-2s", help="Takes data from A2C-Sync agents", action="store_true") - parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") - parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") + parser.add_argument("--a2csync", "-S", help="Takes data from A2C-Sync agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") - parser.add_argument("--test", "-t", help="Takes data from all agents", action="store_true") global args args = parser.parse_args() return args -def plotter_ep_rew_all(scores, label): - plt.plot(scores, label=label) - plt.axhline(y=200.00, color='r') - plt.ylim(0,500) - plt.ylabel('Reward per Episode') - plt.xlabel('Episode') +def plotter_ep_rew_all(ax, scores, label): + ax.plot(scores, label=label) + ax.axhline(y=200, color='r') + ax.set_ylim(0, 500) + ax.set_ylabel('Reward per Episode') + if __name__ == "__main__": args = handleArguments() - - if args.test: - if args.a2c: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - - if args.a3c: - # load array - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - - if args.a2csync: - # load array - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.separatedNN: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - if args.sharedNN: - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.all: - # load array - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - else: - if args.a2c: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - - if args.a3c: - # load array - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - - if args.a2csync: - # load array - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.separatedNN: - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - if args.sharedNN: - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") - - if args.all: - # load array - # load array - a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') - a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') - a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') - - plotter_ep_rew_all(a2c_data, "A2C") - plotter_ep_rew_all(a3c_data, "A3C") - plotter_ep_rew_all(a2c_sync_data, "A2C-Sync") - - # load array - a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') - a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') - a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') - - plotter_ep_rew_all(a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(a3c_comb_data, "A3C (shared NN)") - plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + if args.a2c: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + a2c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') + a2c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax1,a2c_data, "A2C") + plotter_ep_rew_all(ax1, a2c_comb_data, "A2C (shared NN)") + plotter_ep_rew_all(ax2, a2c_data_test, "A2C") + plotter_ep_rew_all(ax2, a2c_comb_data_test, "A2C (shared NN)") + ax2.set_xlabel('Episode') + plt.title("Vanilla A2C-Cartpole", fontsize=16) + + if args.a3c: + # load array + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + a3c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') + a3c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax1,a3c_data, "A3C") + plotter_ep_rew_all(ax1, a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(ax2, a3c_data_test, "A3C") + plotter_ep_rew_all(ax2, a3c_comb_data_test, "A3C (shared NN)") + ax2.set_xlabel('Episode') + plt.title("A3C-Cartpole", fontsize=16) + + if args.a2csync: + # load array + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + plotter_ep_rew_all(ax1, a2c_sync_data, "A2C-Sync") + + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + plotter_ep_rew_all(ax1, a2c_sync_comb_data, "A2C-Sync (shared NN)") + + a2c_sync_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_sync_data_test, "A2C-Sync") + + a2c_sync_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_sync_comb_data_test, "A2C-Sync (shared NN)") + ax2.set_xlabel('Episode') + plt.title("Synchronous A2C-Cartpole", fontsize=16) + + if args.all: + # load array + a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') + a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') + plotter_ep_rew_all(ax1, a2c_data, "A2C") + plotter_ep_rew_all(ax1, a2c_comb_data, "A2C (shared NN)") + + a3c_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', delimiter=',') + a3c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb.csv', delimiter=',') + plotter_ep_rew_all(ax1, a3c_data, "A3C") + plotter_ep_rew_all(ax1, a3c_comb_data, "A3C (shared NN)") + + a2c_sync_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart.csv', delimiter=',') + plotter_ep_rew_all(ax1, a2c_sync_data, "A2C-Sync") + a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') + plotter_ep_rew_all(ax1, a2c_sync_comb_data, "A2C-Sync (shared NN)") + + + a2c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') + a3c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') + a2c_sync_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_data_test, "A2C") + plotter_ep_rew_all(ax2, a3c_data_test, "A3C") + plotter_ep_rew_all(ax2, a2c_sync_data_test, "A2C-Sync") + + # load array + a2c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') + a3c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') + a2c_sync_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb_test.csv', delimiter=',') + plotter_ep_rew_all(ax2, a2c_comb_data_test, "A2C (shared NN)") + plotter_ep_rew_all(ax2, a3c_comb_data_test, "A3C (shared NN)") + plotter_ep_rew_all(ax2, a2c_sync_comb_data_test, "A2C-Sync (shared NN)") + + ax2.set_xlabel('Episode') + plt.title("Advantage Actor-Critic Cartpole", fontsize=16) From 77975c38582c13ef3afb925b36a046b246fcdc0d Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 26 Apr 2020 16:06:42 +0200 Subject: [PATCH 52/63] CART/VIZ - probabilities plot --- CARTPOLE/algo_compare.py | 15 +++++++++++++++ CARTPOLE/cart_utils.py | 19 ++++++++++++++++++- VIZDOOM/algo_compare.py | 17 +++++++++++++++++ VIZDOOM/viz_utils.py | 16 ++++++++++++++++ 4 files changed, 66 insertions(+), 1 deletion(-) diff --git a/CARTPOLE/algo_compare.py b/CARTPOLE/algo_compare.py index 094823e..ffd1dae 100644 --- a/CARTPOLE/algo_compare.py +++ b/CARTPOLE/algo_compare.py @@ -11,6 +11,7 @@ def handleArguments(): parser.add_argument("--a3c", "-3", help="Takes data from A3C agents", action="store_true") parser.add_argument("--a2csync", "-S", help="Takes data from A2C-Sync agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--probs", "-p", help="Plots probabilities of action 'right'", action="store_true") global args args = parser.parse_args() return args @@ -22,6 +23,13 @@ def plotter_ep_rew_all(ax, scores, label): ax.set_ylabel('Reward per Episode') +def plotter_probs(ax, probs): + ax.plot(probs) + ax.set_ylim(0.3,0.7) + ax.axhline(max(probs), color='r') + ax.axhline(min(probs), color='r') + ax.set_ylabel("Probability of Action 'Right'") + if __name__ == "__main__": @@ -106,7 +114,14 @@ def plotter_ep_rew_all(ax, scores, label): ax2.set_xlabel('Episode') plt.title("Advantage Actor-Critic Cartpole", fontsize=16) + if args.probs: + probs = loadtxt('CARTPOLE/cart_save_plot_data/probs.csv', delimiter=',') + probs_test = loadtxt('CARTPOLE/cart_save_plot_data/probs_test.csv', delimiter=',') + plotter_probs(ax1, probs) + plotter_probs(ax2, probs_test) + ax2.set_xlabel('Batch of 100 Actions') + plt.title("Action Probabilities - Cartpole", fontsize=16) plt.legend() plt.show() \ No newline at end of file diff --git a/CARTPOLE/cart_utils.py b/CARTPOLE/cart_utils.py index b623f3a..588c6f3 100644 --- a/CARTPOLE/cart_utils.py +++ b/CARTPOLE/cart_utils.py @@ -104,6 +104,7 @@ def record(global_ep, global_ep_r, ep_r, res_queue, time_queue, global_time_done time_queue.put(time_done) action_queue.put(a) + def plotter_ep_rew_norm(ax2, scores): ax2.plot(scores) ax2.axhline(y=200.00, color='r') @@ -146,6 +147,22 @@ def confidence_intervall(actions, load_model = False): # Check for probabilities of actions and create confidence intervall for two standard deviations print("Probabilities: ", probabilities) + if handleArguments().load_model: + probs = np.asarray([probabilities]) + np.savetxt('CARTPOLE/cart_save_plot_data/probs_test.csv', probs, delimiter=',') + else: + probs = np.asarray([probabilities]) + np.savetxt('CARTPOLE/cart_save_plot_data/probs.csv', probs, delimiter=',') + + plt.figure(3) + plt.plot(probabilities) + plt.ylim(0,1) + plt.axhline(max(probabilities), color='r') + plt.axhline(min(probabilities), color='r') + plt.ylabel("Probability of Choosing Action 'Right'") + plt.xlabel("Batch of 100 Actions") + plt.title("Advantage Actor-Critic: Cartpole", fontsize = 16) + if load_model == True: stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 print("First Confidence Intervall for 95% confidence: the action 'right' is chosen between", @@ -160,7 +177,7 @@ def confidence_intervall(actions, load_model = False): round(probabilities[2] + stan_dev2, 3)) stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + print("Third Confidence Intervall for 95% confidence: the action 'right' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", round(probabilities[3] + stan_dev3, 3)) diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index 7be9f71..9df0a9e 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -24,6 +24,13 @@ def plotter_ep_rew_all(ax, scores, label): ax.set_ylabel('Reward per Episode') +def plotter_probs(ax, probs): + ax.ylim(0,0.4) + ax.axhline(max(probs), color='r') + ax.axhline(min(probs), color='r') + ax.set_ylabel("Probability of Taking Action 'Shoot'") + + if __name__ == "__main__": @@ -142,6 +149,16 @@ def plotter_ep_rew_all(ax, scores, label): plt.title("Advantage Actor-Critic Vizdoom", fontsize=16) + if args.probs: + probs = loadtxt('VIZDOOM/doom_save_plot_data/probs.csv', delimiter=',') + probs_test = loadtxt('VIZDOOM/doom_save_plot_data/probs_test.csv', delimiter=',') + + plotter_probs(ax1, probs) + plotter_probs(ax2, probs_test) + ax2.set_xlabel('Batch of 100 Actions') + ax2.title("Advantage Actor-Critic: Vizdoom", fontsize=16) + + plt.legend() plt.show() \ No newline at end of file diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 12a31f9..cf866e5 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -140,6 +140,22 @@ def confidence_intervall(actions, load_model=False): # Check for probabilities of actions and create confidence intervall for two standard deviations print("Probabilities: ", probabilities) + if handleArguments().load_model: + probs = np.asarray([probabilities]) + np.savetxt('VIZDOOM/doom_save_plot_data/probs_test.csv', probs, delimiter=',') + else: + probs = np.asarray([probabilities]) + np.savetxt('VIZDOOM/doom_save_plot_data/probs.csv', probs, delimiter=',') + + plt.figure(3) + plt.plot(probabilities) + plt.ylim(0,1) + plt.axhline(max(probabilities), color='r') + plt.axhline(min(probabilities), color='r') + plt.ylabel("Probability of Choosing Action 'Shoot'") + plt.xlabel("Batch of 100 Actions") + plt.title("Advantage Actor-Critic: Vizdoom", fontsize = 16) + if load_model == True: stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 print("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", From 220c2ed4a3b35e24f198c75b2e1fd3c05c47062f Mon Sep 17 00:00:00 2001 From: juice1000 Date: Sun, 26 Apr 2020 21:17:41 +0200 Subject: [PATCH 53/63] CART/VIZ - code corrections --- CARTPOLE/a2c_cart.py | 15 ++++++++------- CARTPOLE/a3c_cart.py | 13 ++++++------- VIZDOOM/algo_compare.py | 12 +++++++----- VIZDOOM/vizdoom_a2c.py | 6 +++--- VIZDOOM/vizdoom_a2c_comb.py | 6 +++--- 5 files changed, 27 insertions(+), 25 deletions(-) diff --git a/CARTPOLE/a2c_cart.py b/CARTPOLE/a2c_cart.py index bf015df..cba6ceb 100644 --- a/CARTPOLE/a2c_cart.py +++ b/CARTPOLE/a2c_cart.py @@ -184,12 +184,6 @@ def loss_func(self, s, a, v_t): timedelta_sum += timedelta/3 - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - # Plot results if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) @@ -198,6 +192,12 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + # Get results for confidence interval + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([scores]) @@ -211,7 +211,8 @@ def loss_func(self, s, a, v_t): 'weight': 'normal', 'size': 8 } - #plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) + plt.title("Vanilla A2C-Cartpole", fontsize = 16) plt.show() diff --git a/CARTPOLE/a3c_cart.py b/CARTPOLE/a3c_cart.py index f722452..6ecc25a 100644 --- a/CARTPOLE/a3c_cart.py +++ b/CARTPOLE/a3c_cart.py @@ -207,13 +207,6 @@ def run(self): timedelta_sum += timedelta / 3 - # Get results for confidence intervall - - if handleArguments().load_model: - confidence_intervall(actions, True) - else: - confidence_intervall(actions) - # Plot results if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) @@ -230,6 +223,12 @@ def run(self): scores = np.asarray([res]) np.savetxt('CARTPOLE/cart_save_plot_data/a3c_cart.csv', scores, delimiter=',') + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(actions, True) + else: + confidence_intervall(actions) + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index 9df0a9e..073e403 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -13,6 +13,7 @@ def handleArguments(): parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--probs", "-p", help="Plots probabilities of action 'right'", action="store_true") global args args = parser.parse_args() return args @@ -25,10 +26,11 @@ def plotter_ep_rew_all(ax, scores, label): def plotter_probs(ax, probs): - ax.ylim(0,0.4) + ax.plot(probs) + ax.set_ylim(0,0.1) ax.axhline(max(probs), color='r') ax.axhline(min(probs), color='r') - ax.set_ylabel("Probability of Taking Action 'Shoot'") + ax.set_ylabel("Probability of Action 'Shoot'") @@ -107,8 +109,8 @@ def plotter_probs(ax, probs): if args.all: # load array # load array - a2c_data = loadtxt('VIZDOOM/cart_save_plot_data/a2c_doom.csv', delimiter=',') - a3c_data = loadtxt('VIZDOOM/cart_save_plot_data/a3c_doom.csv', delimiter=',') + a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') + a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') a2c_sync_arr = [] for i in range(1, 44): a2c_sync_data = loadtxt(f'VIZDOOM/doom_save_plot_data/A2C/a2c_sync_doom{i}.csv', delimiter=',') @@ -156,7 +158,7 @@ def plotter_probs(ax, probs): plotter_probs(ax1, probs) plotter_probs(ax2, probs_test) ax2.set_xlabel('Batch of 100 Actions') - ax2.title("Advantage Actor-Critic: Vizdoom", fontsize=16) + plt.title("Advantage Actor-Critic: Vizdoom", fontsize=16) diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index d46baff..cf26c80 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -241,9 +241,6 @@ def loss_func(self, s, a, v_t): print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) timedelta_sum += timedelta/3 - # Get results for confidence intervall - confidence_intervall(action) - # Plot results if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) @@ -252,6 +249,9 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + # Get results for confidence intervall + confidence_intervall(action) + if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([scores]) diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py index 2d37d45..8eb8574 100644 --- a/VIZDOOM/vizdoom_a2c_comb.py +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -239,9 +239,6 @@ def loss_func(self, s, a, v_t): print("Number of Episodes: ", global_ep, " | Finished within: ", timedelta) timedelta_sum += timedelta/3 - # Get results for confidence intervall - confidence_intervall(action) - # Plot results if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) @@ -250,6 +247,9 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + # Get results for confidence intervall + confidence_intervall(action) + if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([scores]) From 5bc01fc84ce96432fe4094ffd124b65d6f689de4 Mon Sep 17 00:00:00 2001 From: juice1000 Date: Fri, 1 May 2020 15:35:31 +0200 Subject: [PATCH 54/63] VIZDOOM - fix probabilities plot and ep-rew-time plot, fix action_queue in A3C --- VIZDOOM/viz_utils.py | 3 +-- VIZDOOM/vizdoom_a2c.py | 7 +++--- VIZDOOM/vizdoom_a2c_comb.py | 8 +++---- VIZDOOM/vizdoom_a3c.py | 45 +++++++++++++++++-------------------- VIZDOOM/vizdoom_a3c_comb.py | 41 +++++++++++++++++---------------- 5 files changed, 48 insertions(+), 56 deletions(-) diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index cf866e5..6cebabd 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -131,7 +131,7 @@ def confidence_intervall(actions, load_model=False): for a in actions: if a == 1: probab_count += 1 - if count % 100 == 0 and count != 1: + if count % 10 == 0 and count != 1: probab_count = probab_count / 100 probabilities.append(probab_count) probab_count = 0 @@ -154,7 +154,6 @@ def confidence_intervall(actions, load_model=False): plt.axhline(min(probabilities), color='r') plt.ylabel("Probability of Choosing Action 'Shoot'") plt.xlabel("Batch of 100 Actions") - plt.title("Advantage Actor-Critic: Vizdoom", fontsize = 16) if load_model == True: stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index cf26c80..6277aca 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall from shared_adam import SharedAdam import os os.environ["OMP_NUM_THREADS"] = "1" @@ -249,6 +248,8 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + plt.title("Vanilla A2C-Vizdoom", fontsize=16) + # Get results for confidence intervall confidence_intervall(action) @@ -267,7 +268,7 @@ def loss_func(self, s, a, v_t): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Vizdoom", fontsize = 16) + plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py index 8eb8574..367f68a 100644 --- a/VIZDOOM/vizdoom_a2c_comb.py +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall from shared_adam import SharedAdam import os os.environ["OMP_NUM_THREADS"] = "1" @@ -71,8 +70,6 @@ def __init__(self, a_dim): self.v2 = nn.Linear(240, 360) self.v3 = nn.Linear(360, 1) - #self.optimizer = torch.optim.SGD(self.parameters(), FLAGS.learning_rate) - set_init([self.pi1, self.pi2, self.pi3, self.v2, self.v3]) self.distribution = torch.distributions.Categorical @@ -247,6 +244,8 @@ def loss_func(self, s, a, v_t): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, scores) + plt.title("Vanilla A2C-Vizdoom (shared NN)", fontsize=16) + # Get results for confidence intervall confidence_intervall(action) @@ -265,7 +264,6 @@ def loss_func(self, s, a, v_t): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Vanilla A2C-Vizdoom (shared NN)", fontsize = 16) plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a3c.py b/VIZDOOM/vizdoom_a3c.py index 8a89d4f..81c95a9 100644 --- a/VIZDOOM/vizdoom_a3c.py +++ b/VIZDOOM/vizdoom_a3c.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -172,7 +171,7 @@ def run(self): if done or total_step % UPDATE_GLOBAL_ITER == 0: # update network # sync push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - game.get_total_reward() + #game.get_total_reward() buffer_s, buffer_a, buffer_r = [], [], [] if done: @@ -186,8 +185,6 @@ def run(self): state = s_ total_step += 1 - - print("Out of here") self.time_queue.put(None) self.res_queue.put(None) self.action_queue.put(None) @@ -222,6 +219,7 @@ def run(self): opt = SharedAdam(model.parameters(), lr=0.001, betas=(0.92, 0.999)) global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() + # parallel training if handleArguments().load_model: workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] @@ -238,20 +236,18 @@ def run(self): while True: r = res_queue.get() t = time_queue.get() - a = action_queue.get() - - if r is not None: - res.append(r) - if t is not None: - durations.append(t) - if a is not None: - action.append(a) - else: + act = action_queue.get() + + if r is None or t is None or act is None: break + res.append(r) + durations.append(t) + action.append(act) + [w.join() for w in workers] - if not handleArguments().load_model and global_ep.value >= 10: + if not handleArguments().load_model: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom.pt") elif handleArguments().load_model: @@ -262,14 +258,7 @@ def run(self): print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) timedelta_sum += timedelta / 3 - # Get results for confidence intervall - # if handleArguments().load_model: - # confidence_intervall(action, True) - # else: - # confidence_intervall(action) - # Plot results - if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) plotter_ep_rew_norm(ax2, res) @@ -277,6 +266,8 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + plt.title("A3C-Vizdoom", fontsize=16) + if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([res]) @@ -285,6 +276,12 @@ def run(self): scores = np.asarray([res]) np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', scores, delimiter=',') + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(action, True) + else: + confidence_intervall(action) + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -292,9 +289,7 @@ def run(self): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - #else: - # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Vizdoom", fontsize=16) + plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py index 4e6d62d..fed6062 100644 --- a/VIZDOOM/vizdoom_a3c_comb.py +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -138,10 +137,9 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu def run(self): total_step = 1 - stop_processes = False scores = [] - while self.g_ep.value < MAX_EP and stop_processes is False: + while self.g_ep.value < MAX_EP: self.game.new_episode() state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] @@ -150,6 +148,7 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) + if a in attack: self.action_queue.put(1) else: @@ -184,6 +183,7 @@ def run(self): self.time_queue.put(None) self.res_queue.put(None) + self.action_queue.put(None) if __name__ == '__main__': @@ -231,16 +231,15 @@ def run(self): while True: r = res_queue.get() t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - if t is not None: - durations.append(t) - if a is not None: - action.append(a) - else: + act = action_queue.get() + + if r is None or t is None or act is None: break + res.append(r) + durations.append(t) + action.append(act) + [w.join() for w in workers] if not handleArguments().load_model and global_ep.value >= 10: @@ -254,12 +253,6 @@ def run(self): print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) timedelta_sum += timedelta / 3 - # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(action, True) - else: - confidence_intervall(action) - # Plot results if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) @@ -268,6 +261,8 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + plt.title("A3C-Vizdoom (shared NN)", fontsize=16) + if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([res]) @@ -276,6 +271,12 @@ def run(self): scores = np.asarray([res]) np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', scores, delimiter=',') + # Get results for confidence intervall + if handleArguments().load_model: + confidence_intervall(action, True) + else: + confidence_intervall(action) + font = {'family': 'serif', 'color': 'darkred', 'weight': 'normal', @@ -283,9 +284,7 @@ def run(self): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - #else: - # plt.text(0, 500, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("A3C-Vizdoom (shared NN)", fontsize=16) + plt.show() game.close() From 983bc7c141924fa8e9bf1633ed9302350950effb Mon Sep 17 00:00:00 2001 From: juice1000 Date: Mon, 4 May 2020 16:57:02 +0200 Subject: [PATCH 55/63] VIZDOOM - code grooming and refactor algo_compare --- CARTPOLE/algo_compare.py | 19 ++++- VIZDOOM/algo_compare.py | 26 ++++-- VIZDOOM/viz_utils.py | 22 ++--- VIZDOOM/vizdoom_a2c.py | 22 ++--- VIZDOOM/vizdoom_a2c_comb.py | 20 ++--- VIZDOOM/vizdoom_a2c_sync.py | 136 +++++++++-------------------- VIZDOOM/vizdoom_a2c_sync_comb.py | 142 ++++++++++--------------------- VIZDOOM/vizdoom_a3c_comb.py | 14 +-- 8 files changed, 147 insertions(+), 254 deletions(-) diff --git a/CARTPOLE/algo_compare.py b/CARTPOLE/algo_compare.py index ffd1dae..c1e7f9e 100644 --- a/CARTPOLE/algo_compare.py +++ b/CARTPOLE/algo_compare.py @@ -11,6 +11,7 @@ def handleArguments(): parser.add_argument("--a3c", "-3", help="Takes data from A3C agents", action="store_true") parser.add_argument("--a2csync", "-S", help="Takes data from A2C-Sync agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--alltest", "-t", help="Takes data from all trained agents", action="store_true") parser.add_argument("--probs", "-p", help="Plots probabilities of action 'right'", action="store_true") global args args = parser.parse_args() @@ -34,7 +35,8 @@ def plotter_probs(ax, probs): if __name__ == "__main__": args = handleArguments() - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + if not args.all and args.alltest: + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) if args.a2c: # load array @@ -79,6 +81,10 @@ def plotter_probs(ax, probs): plt.title("Synchronous A2C-Cartpole", fontsize=16) if args.all: + + + fig, (ax1) = plt.subplots() + # load array a2c_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart.csv', delimiter=',') a2c_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb.csv', delimiter=',') @@ -95,7 +101,7 @@ def plotter_probs(ax, probs): a2c_sync_comb_data = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_comb.csv', delimiter=',') plotter_ep_rew_all(ax1, a2c_sync_comb_data, "A2C-Sync (shared NN)") - + plt.subplot(ax2) a2c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_test.csv', delimiter=',') a3c_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_test.csv', delimiter=',') a2c_sync_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_sync_cart_test.csv', delimiter=',') @@ -103,6 +109,11 @@ def plotter_probs(ax, probs): plotter_ep_rew_all(ax2, a3c_data_test, "A3C") plotter_ep_rew_all(ax2, a2c_sync_data_test, "A2C-Sync") + plt.title("Advantage Actor-Critic Cartpole", fontsize=16) + plt.legend() + + if args.alltest: + fig, (ax2) = plt.subplots() # load array a2c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a2c_cart_comb_test.csv', delimiter=',') a3c_comb_data_test = loadtxt('CARTPOLE/cart_save_plot_data/a3c_cart_comb_test.csv', delimiter=',') @@ -113,6 +124,7 @@ def plotter_probs(ax, probs): ax2.set_xlabel('Episode') plt.title("Advantage Actor-Critic Cartpole", fontsize=16) + plt.legend() if args.probs: probs = loadtxt('CARTPOLE/cart_save_plot_data/probs.csv', delimiter=',') @@ -123,5 +135,6 @@ def plotter_probs(ax, probs): ax2.set_xlabel('Batch of 100 Actions') plt.title("Action Probabilities - Cartpole", fontsize=16) - plt.legend() + if not args.all and args.alltest: + plt.legend() plt.show() \ No newline at end of file diff --git a/VIZDOOM/algo_compare.py b/VIZDOOM/algo_compare.py index 073e403..46ad00d 100644 --- a/VIZDOOM/algo_compare.py +++ b/VIZDOOM/algo_compare.py @@ -13,6 +13,7 @@ def handleArguments(): parser.add_argument("--separatedNN", "-se", help="Takes data from separated NN agents", action="store_true") parser.add_argument("--sharedNN", "-sh", help="Takes data from shared NN agents", action="store_true") parser.add_argument("--all", "-a", help="Takes data from all agents", action="store_true") + parser.add_argument("--alltest", "-t", help="Takes data from all trained agents", action="store_true") parser.add_argument("--probs", "-p", help="Plots probabilities of action 'right'", action="store_true") global args args = parser.parse_args() @@ -27,7 +28,7 @@ def plotter_ep_rew_all(ax, scores, label): def plotter_probs(ax, probs): ax.plot(probs) - ax.set_ylim(0,0.1) + ax.set_ylim(0,0.2) ax.axhline(max(probs), color='r') ax.axhline(min(probs), color='r') ax.set_ylabel("Probability of Action 'Shoot'") @@ -37,7 +38,9 @@ def plotter_probs(ax, probs): if __name__ == "__main__": args = handleArguments() - fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) + + if not args.all and not args.alltest: + fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True) if args.a2c: # load array @@ -107,7 +110,8 @@ def plotter_probs(ax, probs): plotter_ep_rew_all(a2c_sync_comb_data, "A2C-Sync (shared NN)") if args.all: - # load array + + fig, (ax1) = plt.subplots() # load array a2c_data = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom.csv', delimiter=',') a3c_data = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom.csv', delimiter=',') @@ -133,6 +137,13 @@ def plotter_probs(ax, probs): plotter_ep_rew_all(ax1, a2c_comb_data, "A2C (shared NN)") plotter_ep_rew_all(ax1,a3c_comb_data, "A3C (shared NN)") plotter_ep_rew_all(ax1, a2c_sync_comb_arr, "A2C-Sync (shared NN)") + + plt.title("Advantage Actor-Critic Vizdoom", fontsize=16) + plt.legend() + + if args.alltest: + fig,(ax2) = plt.subplots() + a2c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_test.csv', delimiter=',') a3c_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_test.csv', delimiter=',') a2c_sync_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_test.csv', delimiter=',') @@ -143,12 +154,13 @@ def plotter_probs(ax, probs): a2c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_doom_comb_test.csv', delimiter=',') a3c_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb_test.csv', delimiter=',') a2c_sync_comb_data_test = loadtxt('VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv', delimiter=',') - plotter_ep_rew_all(ax2, a2c_comb_data, "A2C (shared NN)") - plotter_ep_rew_all(ax2, a3c_comb_data, "A3C (shared NN)") + plotter_ep_rew_all(ax2, a2c_comb_data_test, "A2C (shared NN)") + plotter_ep_rew_all(ax2, a3c_comb_data_test, "A3C (shared NN)") plotter_ep_rew_all(ax2, a2c_sync_comb_data_test, "A2C-Sync (shared NN)") ax2.set_xlabel('Episode') plt.title("Advantage Actor-Critic Vizdoom", fontsize=16) + plt.legend() if args.probs: @@ -160,7 +172,7 @@ def plotter_probs(ax, probs): ax2.set_xlabel('Batch of 100 Actions') plt.title("Advantage Actor-Critic: Vizdoom", fontsize=16) + if not args.all and not args.alltest: + plt.legend() - - plt.legend() plt.show() \ No newline at end of file diff --git a/VIZDOOM/viz_utils.py b/VIZDOOM/viz_utils.py index 6cebabd..2d31f71 100644 --- a/VIZDOOM/viz_utils.py +++ b/VIZDOOM/viz_utils.py @@ -155,22 +155,16 @@ def confidence_intervall(actions, load_model=False): plt.ylabel("Probability of Choosing Action 'Shoot'") plt.xlabel("Batch of 100 Actions") - if load_model == True: - stan_dev1 = np.sqrt(probabilities[0] * (1 - probabilities[0]) / 100) * 2 - print("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", - round(probabilities[0] - stan_dev1, 3), " and", round(probabilities[0] + stan_dev1, 3)) + stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 + print ("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) - else: - stan_dev1 = np.sqrt(probabilities[1] * (1-probabilities[1]) / 100)*2 - print ("First Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[1]-stan_dev1, 3), " and", round(probabilities[1] + stan_dev1, 3)) - - stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", - round(probabilities[2] + stan_dev2, 3)) + stan_dev2 = np.sqrt(probabilities[2] * (1 - probabilities[2]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[2] - stan_dev2, 3), " and", + round(probabilities[2] + stan_dev2, 3)) - stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 - print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", - round(probabilities[3] + stan_dev3, 3)) + stan_dev3 = np.sqrt(probabilities[3] * (1 - probabilities[3]) /100) * 2 + print("Second Confidence Intervall for 95% confidence: the action 'attack' is chosen between", round(probabilities[3] - stan_dev3, 3), " and", + round(probabilities[3] + stan_dev3, 3)) def handleArguments(): diff --git a/VIZDOOM/vizdoom_a2c.py b/VIZDOOM/vizdoom_a2c.py index 6277aca..b995e63 100644 --- a/VIZDOOM/vizdoom_a2c.py +++ b/VIZDOOM/vizdoom_a2c.py @@ -126,7 +126,6 @@ def loss_func(self, s, a, v_t): return total_loss - if __name__ == '__main__': print ("Starting A2C Agent for Vizdoom-DeadlyCorridor") @@ -158,7 +157,7 @@ def loss_func(self, s, a, v_t): # Global variables for episodes durations = [] scores = [] - global_ep, global_ep_r, global_time_done = 1, 0., 0. + global_ep, global_ep_r, global_time_done = 0, 0., 0. name = 'w00' total_step = 1 stop_processes = False @@ -175,12 +174,11 @@ def loss_func(self, s, a, v_t): done = False a = model.choose_action(state) - #print ("Action", a) - for i in range(len(attack)): - if attack[i] == a: - action.append(1) - else: - action.append(0) + + if a in attack: + action.append(1) + else: + action.append(0) r = game.make_action(actions[a], frame_repeat) @@ -227,13 +225,11 @@ def loss_func(self, s, a, v_t): state = s_ total_step += 1 - if not handleArguments().load_model and global_ep >= 10: + if handleArguments().load_model: + print("Testing! No need to save model.") + else: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime diff --git a/VIZDOOM/vizdoom_a2c_comb.py b/VIZDOOM/vizdoom_a2c_comb.py index 367f68a..e8322f6 100644 --- a/VIZDOOM/vizdoom_a2c_comb.py +++ b/VIZDOOM/vizdoom_a2c_comb.py @@ -123,7 +123,6 @@ def loss_func(self, s, a, v_t): return total_loss - if __name__ == '__main__': print ("Starting A2C Agent for Vizdoom-DeadlyCorridor") @@ -155,7 +154,7 @@ def loss_func(self, s, a, v_t): # Global variables for episodes durations = [] scores = [] - global_ep, global_ep_r, global_time_done = 1, 0., 0. + global_ep, global_ep_r, global_time_done = 0, 0., 0. name = 'w00' total_step = 1 stop_processes = False @@ -172,11 +171,10 @@ def loss_func(self, s, a, v_t): done = False a = model.choose_action(state) - for i in range(len(attack)): - if attack[i] == a: - action.append(1) - else: - action.append(0) + if a in attack: + action.append(1) + else: + action.append(0) r = game.make_action(actions[a], frame_repeat) @@ -223,13 +221,11 @@ def loss_func(self, s, a, v_t): state = s_ total_step += 1 - if not handleArguments().load_model and global_ep >= 10: + if handleArguments().load_model: + print("Testing! No need to save model.") + else: print("Save model") torch.save(model, "./VIZDOOM/doom_save_model/a2c_doom_comb.pt") - elif handleArguments().load_model: - print ("Testing! No need to save model.") - else: - print("Failed to train agent. Model was not saved") endtime = datetime.now() timedelta = endtime - starttime diff --git a/VIZDOOM/vizdoom_a2c_sync.py b/VIZDOOM/vizdoom_a2c_sync.py index ad5ad84..5431267 100644 --- a/VIZDOOM/vizdoom_a2c_sync.py +++ b/VIZDOOM/vizdoom_a2c_sync.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -56,6 +55,11 @@ def game_state(game): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) print("Number of used CPUs: ", worker_num) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -122,7 +126,6 @@ def loss_func(self, s, a, v_t): return total_loss - class Worker(mp.Process): def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() @@ -133,28 +136,32 @@ def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done self.game = initialize_vizdoom(config_file_path) self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.inner_ep = inner_ep - #self.sleep_max = sleep_max def run(self): total_step = 1 - stop_processes = False - scores = [] - sleep = 15 - superdone = 0 - cpu_count = [] - while self.inner_ep.value < (mp.cpu_count()*3) and stop_processes is False: + + if handleArguments().load_model: + INNER_MAX_EP = MAX_EP + else: + INNER_MAX_EP = mp.cpu_count()*3 + + while self.inner_ep.value < INNER_MAX_EP: self.game.new_episode() state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. - #print("Outside:", sleep) print("initialized:", self.name) while True: start = time.time() done = False a = self.lnet.choose_action(state) + if a in attack: + self.action_queue.put(1) + else: + self.action_queue.put(0) + r = self.game.make_action(actions[a], frame_repeat) if self.game.is_episode_finished(): @@ -171,71 +178,20 @@ def run(self): # sync optimize(opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - #game.get_total_reward() - #buffer_s, buffer_a, buffer_r = [], [], [] + buffer_s, buffer_a, buffer_r = [], [], [] if done: - #print("done with:", self.name) self.inner_ep.value += 1 print ("Inner Ep:", self.inner_ep.value) end = time.time() time_done = end - start - #print ("Duration:", time_done) - #print("Whats the time?", sleep) - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, - self.action_queue, self.name) - #print("hore...") - #push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) - #time.sleep(20) - #print(self.sync.get()) - #print("Pull global model") - #self.lnet.load_state_dict(self.gnet.state_dict()) - #print("here...") + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, self.name) for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): gp._grad = lp.grad - #if self.sync.get() == True: - # print("Agent", self.name, "gets in sync") - # self.agent_count.append(1) - # while len(self.agent_count) != mp.cpu_count(): - # print(len(self.agent_count), self.agent_count) - # time.sleep(15) -# - # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): - # gp._grad = lp.grad -# - # print("loading state dict") - # self.lnet.load_state_dict(self.gnet.state_dict()) - # if len(self.agent_count) >= 12: - # self.agent_count = [] - # break - - - #if superdone % 10 == 0: -# - # sleep = 120 - 10 * len(self.sync) - # print(sleep) - # print("Pull global model") - # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): - # gp._grad = lp.grad - # opt.step() - # time.sleep(sleep) - # self.lnet.load_state_dict(self.gnet.state_dict()) - # if len(self.sync) == mp.cpu_count(): - # self.sync = [] - # break - scores.append(int(self.g_ep_r.value)) - - if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: - stop_processes = True - elif handleArguments().normalized_plot: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - else: - stop_processes = False break state = s_ @@ -280,7 +236,7 @@ def run(self): action = [] global_ep, global_ep_r, global_time_done = mp.Value('i', 0), mp.Value('d', 0.), mp.Value('d', 0.) res_queue, time_queue, action_queue = mp.Queue(), mp.Queue(), mp.Queue() - loop = 20 + loop = 0 while global_ep.value < MAX_EP: loop += 1 @@ -290,43 +246,34 @@ def run(self): manager = mp.Manager() sync, agent_count = mp.Queue(), manager.list() if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i, sync) for i in range(1)] + workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] [w.start() for w in workers] else: workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in range(worker_num)] [w.start() for w in workers] - while True: - #sync.put(False) - #if global_ep.value % (mp.cpu_count()*3) == 0 and global_ep.value != 0: - # for w in workers: - # sync.put(True) r = res_queue.get() t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - if t is not None: - durations.append(t) - if a is not None: - action.append(a) - else: + act = action_queue.get() + + if r is None or t is None or act is None: break + res.append(r) + durations.append(t) + action.append(act) for w in workers: w.join() - w.terminate() + #w.terminate() - if not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom.pt") - elif handleArguments().load_model: + if handleArguments().load_model: print("Testing! No need to save model.") else: - print("Failed to train agent. Model was not saved") + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom.pt") if handleArguments().save_data: if handleArguments().load_model: @@ -339,16 +286,11 @@ def run(self): endtime = datetime.now() timedelta = endtime - starttime print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - #timedelta_sum += timedelta / 3 - # Get results for confidence intervall - # if handleArguments().load_model: - # confidence_intervall(action, True) - # else: - # confidence_intervall(action) + if handleArguments().load_model: + timedelta_sum += timedelta / 3 # Plot results - if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) plotter_ep_rew_norm(ax2, res) @@ -356,6 +298,10 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + plt.title("Synchronous A2C-Vizdoom", fontsize=16) + + # Get results for confidence intervall + confidence_intervall(action) font = {'family': 'serif', 'color': 'darkred', @@ -364,9 +310,7 @@ def run(self): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - #else: - # plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C-Vizdoom", fontsize=16) + plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a2c_sync_comb.py b/VIZDOOM/vizdoom_a2c_sync_comb.py index 238c42b..0949e28 100644 --- a/VIZDOOM/vizdoom_a2c_sync_comb.py +++ b/VIZDOOM/vizdoom_a2c_sync_comb.py @@ -7,10 +7,9 @@ import torch from torch import nn import torch.nn.functional as F -from tqdm import trange from vizdoom import DoomGame, Mode, ScreenFormat, ScreenResolution -from viz_utils import v_wrap, set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, push_and_pull, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall +from viz_utils import set_init, plotter_ep_rew, plotter_ep_rew_norm, handleArguments, optimize, record, plotter_ep_time_norm, plotter_ep_time, confidence_intervall import torch.multiprocessing as mp from shared_adam import SharedAdam import sys @@ -56,6 +55,11 @@ def game_state(game): print("All possible Actions:", actions, "\n", "Total: ", len(actions)) print("Number of used CPUs: ", worker_num) +attack = [] +for a in range(len(actions)): + if actions[a][2] == 1: + attack.append(a) + class Net(nn.Module): def __init__(self, a_dim): super(Net, self).__init__() @@ -120,7 +124,6 @@ def loss_func(self, s, a, v_t): return total_loss - class Worker(mp.Process): def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, name): super(Worker, self).__init__() @@ -131,28 +134,32 @@ def __init__(self, gnet, opt, global_ep, inner_ep, global_ep_r, global_time_done self.game = initialize_vizdoom(config_file_path) self.res_queue, self.time_queue, self.action_queue = res_queue, time_queue, action_queue self.inner_ep = inner_ep - #self.sleep_max = sleep_max def run(self): total_step = 1 - stop_processes = False - scores = [] - sleep = 15 - superdone = 0 - cpu_count = [] - while self.inner_ep.value < (mp.cpu_count()*3) and stop_processes is False: + + if handleArguments().load_model: + INNER_MAX_EP = MAX_EP + else: + INNER_MAX_EP = mp.cpu_count()*3 + + while self.inner_ep.value < INNER_MAX_EP: self.game.new_episode() state = game_state(self.game) buffer_s, buffer_a, buffer_r = [], [], [] ep_r = 0. - #print("Outside:", sleep) print("initialized:", self.name) while True: start = time.time() done = False a = self.lnet.choose_action(state) + if a in attack: + self.action_queue.put(1) + else: + self.action_queue.put(0) + r = self.game.make_action(actions[a], frame_repeat) if self.game.is_episode_finished(): @@ -169,71 +176,20 @@ def run(self): # sync optimize(opt, self.lnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA) - #game.get_total_reward() - #buffer_s, buffer_a, buffer_r = [], [], [] + buffer_s, buffer_a, buffer_r = [], [], [] if done: - #print("done with:", self.name) self.inner_ep.value += 1 print ("Inner Ep:", self.inner_ep.value) end = time.time() time_done = end - start - #print ("Duration:", time_done) - #print("Whats the time?", sleep) - record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, a, - self.action_queue, self.name) - #print("hore...") - #push_and_pull(opt, self.lnet, self.gnet, done, s_, buffer_s, buffer_a, buffer_r, GAMMA, True, self.g_ep) - #time.sleep(20) - #print(self.sync.get()) - #print("Pull global model") - #self.lnet.load_state_dict(self.gnet.state_dict()) - #print("here...") + + record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, self.name) for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): gp._grad = lp.grad - #if self.sync.get() == True: - # print("Agent", self.name, "gets in sync") - # self.agent_count.append(1) - # while len(self.agent_count) != mp.cpu_count(): - # print(len(self.agent_count), self.agent_count) - # time.sleep(15) -# - # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): - # gp._grad = lp.grad -# - # print("loading state dict") - # self.lnet.load_state_dict(self.gnet.state_dict()) - # if len(self.agent_count) >= 12: - # self.agent_count = [] - # break - - - #if superdone % 10 == 0: -# - # sleep = 120 - 10 * len(self.sync) - # print(sleep) - # print("Pull global model") - # for lp, gp in zip(self.lnet.parameters(), self.gnet.parameters()): - # gp._grad = lp.grad - # opt.step() - # time.sleep(sleep) - # self.lnet.load_state_dict(self.gnet.state_dict()) - # if len(self.sync) == mp.cpu_count(): - # self.sync = [] - # break - scores.append(int(self.g_ep_r.value)) - - if handleArguments().load_model and handleArguments().normalized_plot: - if np.mean(scores[-min(100, len(scores)):]) >= 50 and self.g_ep.value >= 100: - stop_processes = True - elif handleArguments().normalized_plot: - if np.mean(scores[-min(mp.cpu_count(), len(scores)):]) >= 50 and self.g_ep.value >= mp.cpu_count(): - stop_processes = True - else: - stop_processes = False break state = s_ @@ -288,65 +244,51 @@ def run(self): manager = mp.Manager() sync, agent_count = mp.Queue(), manager.list() if handleArguments().load_model: - workers = [Worker(model, opt, global_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i, sync) for i in range(1)] + workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done,res_queue, time_queue, action_queue, i) for i in range(1)] [w.start() for w in workers] else: workers = [Worker(model, opt, global_ep, inner_ep, global_ep_r, global_time_done, res_queue, time_queue, action_queue, i) for i in range(worker_num)] [w.start() for w in workers] - while True: - #sync.put(False) - #if global_ep.value % (mp.cpu_count()*3) == 0 and global_ep.value != 0: - # for w in workers: - # sync.put(True) r = res_queue.get() t = time_queue.get() - a = action_queue.get() - if r is not None: - res.append(r) - if t is not None: - durations.append(t) - if a is not None: - action.append(a) - else: + act = action_queue.get() + + if r is None or t is None or act is None: break + res.append(r) + durations.append(t) + action.append(act) for w in workers: w.join() - w.terminate() + #w.terminate() - if not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom_comb.pt") - elif handleArguments().load_model: + if handleArguments().load_model: print("Testing! No need to save model.") else: - print("Failed to train agent. Model was not saved") + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a2c_sync_doom_comb.pt") if handleArguments().save_data: if handleArguments().load_model: scores = np.asarray([res]) - np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test{loop}.csv", scores, delimiter=',') + np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb_test.csv", scores, delimiter=',') else: scores = np.asarray([res]) np.savetxt(f"VIZDOOM/doom_save_plot_data/a2c_sync_doom_comb{loop}.csv", scores, delimiter=',') - endtime = datetime.now() - timedelta = endtime - starttime - print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - timedelta_sum += timedelta / 3 + endtime = datetime.now() + timedelta = endtime - starttime + print("Number of Episodes: ", global_ep.value, " | Finished within: ", timedelta) - # Get results for confidence intervall - # if handleArguments().load_model: - # confidence_intervall(action, True) - # else: - # confidence_intervall(action) + if handleArguments().load_model: + timedelta_sum += timedelta / 3 # Plot results - if handleArguments().normalized_plot: plotter_ep_time_norm(ax1, durations) plotter_ep_rew_norm(ax2, res) @@ -354,6 +296,10 @@ def run(self): plotter_ep_time(ax1, durations) plotter_ep_rew(ax2, res) + plt.title("Synchronous A2C Vizdoom (shared NN)", fontsize=16) + + # Get results for action probabilities + confidence_intervall(action) font = {'family': 'serif', 'color': 'darkred', @@ -362,9 +308,7 @@ def run(self): } if handleArguments().normalized_plot: plt.text(0, 450, f"Average Training Duration: {timedelta_sum}", fontdict=font) - #else: - # plt.text(0, 400, f"Average Training Duration: {timedelta_sum}", fontdict=font) - plt.title("Synchronous A2C Vizdoom (shared NN)", fontsize=16) + plt.show() game.close() diff --git a/VIZDOOM/vizdoom_a3c_comb.py b/VIZDOOM/vizdoom_a3c_comb.py index fed6062..07e0843 100644 --- a/VIZDOOM/vizdoom_a3c_comb.py +++ b/VIZDOOM/vizdoom_a3c_comb.py @@ -137,7 +137,6 @@ def __init__(self, gnet, opt, global_ep, global_ep_r, global_time_done, res_queu def run(self): total_step = 1 - scores = [] while self.g_ep.value < MAX_EP: self.game.new_episode() @@ -148,7 +147,6 @@ def run(self): start = time.time() done = False a = self.lnet.choose_action(state) - if a in attack: self.action_queue.put(1) else: @@ -175,7 +173,6 @@ def run(self): end = time.time() time_done = end - start record(self.g_ep, self.g_ep_r, ep_r, self.res_queue, self.time_queue, self.g_time, time_done, self.name) - scores.append(int(self.g_ep_r.value)) break state = s_ @@ -242,11 +239,11 @@ def run(self): [w.join() for w in workers] - if not handleArguments().load_model and global_ep.value >= 10: - print("Save model") - torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") if handleArguments().load_model: print("Testing! No need to save model.") + else: + print("Save model") + torch.save(model, "./VIZDOOM/doom_save_model/a3c_doom_comb.pt") endtime = datetime.now() timedelta = endtime - starttime @@ -272,10 +269,7 @@ def run(self): np.savetxt('VIZDOOM/doom_save_plot_data/a3c_doom_comb.csv', scores, delimiter=',') # Get results for confidence intervall - if handleArguments().load_model: - confidence_intervall(action, True) - else: - confidence_intervall(action) + confidence_intervall(action) font = {'family': 'serif', 'color': 'darkred', From 5a1fb36cebfd60695b63979c3f6d556629bd8089 Mon Sep 17 00:00:00 2001 From: Julien Look <53651363+juice1000@users.noreply.github.com> Date: Sun, 31 May 2020 12:26:09 +0200 Subject: [PATCH 56/63] Update README.md --- README.md | 37 ++++++++++++++----------------------- 1 file changed, 14 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 2b6fae9..8a6920b 100644 --- a/README.md +++ b/README.md @@ -1,17 +1,14 @@ -# Simple implementation of Reinforcement Learning (A3C) using Pytorch +## Experiments that compare Actor-Critic algorithms such as A2C and A3C. A thesis conducted by the TU Berlin. -This is a toy example of using multiprocessing in Python to asynchronously train a -neural network to play discrete action [CartPole](https://gym.openai.com/envs/CartPole-v0/) and -continuous action [Pendulum](https://gym.openai.com/envs/Pendulum-v0/) games. -The asynchronous algorithm I used is called [Asynchronous Advantage Actor-Critic](https://arxiv.org/pdf/1602.01783.pdf) or A3C. - -I believe it would be the simplest toy implementation you can find at the moment (2018-01). +This is an example of using multiprocessing with Pycharm to synchronously and asynchronously train a +neural network to play 2D discrete action [CartPole](https://gym.openai.com/envs/CartPole-v0/) and +3D realistic environment [Vizdoom](https://github.com/mwydmuch/ViZDoom) games. The baseline of the Advantage Actor-Critic is A2C. The asynchronous variation I used is called [Asynchronous Advantage Actor-Critic](https://arxiv.org/pdf/1602.01783.pdf) or A3C. The synchronous version is called [Synchronous Advantage Actor-Critic](https://openai.com/blog/baselines-acktr-a2c/) or A2C-Sync. ## What are the main focuses in this implementation? -* Pytorch + multiprocessing (NOT threading) for parallel training -* Both discrete and continuous action environments -* To be simple and easy to dig into the code (less than 200 lines) +* Pytorch + multiprocessing + shared memory for parallel training +* Simple discrete environment and 3D realistic environment +* Partly shared or separate Actor-Critic Neural Networks ## Reason of using [Pytorch](http://pytorch.org/) instead of [Tensorflow](https://www.tensorflow.org/) @@ -23,24 +20,18 @@ However, the distributed version is for cluster computing which I don't have. When using only one machine, it is slower than threading version I wrote. Fortunately, Pytorch gets the [multiprocessing compatibility](http://pytorch.org/docs/master/notes/multiprocessing.html). -I went through many Pytorch A3C examples ([there](https://github.com/ikostrikov/pytorch-a3c), [there](https://github.com/jingweiz/pytorch-rl) -and [there](https://github.com/ShangtongZhang/DeepRL)). They are great but too complicated to dig into the code. -Therefore, this is my motivation to write my simple example codes. - -BTW, if you are interested to learn Pytorch, [there](https://github.com/MorvanZhou/PyTorch-Tutorial) - is my simple tutorial code with many visualizations. I also made the tensorflow tutorial (same as pytorch) available in [here](https://github.com/MorvanZhou/Tensorflow-Tutorial). ## Codes & Results -* [shared_adam.py](/shared_adam.py): optimizer that shares its parameters in parallel -* [utils.py](/utils.py): useful function that can be used more than once -* [discrete_A3C.py](/discrete_A3C.py): CartPole, neural net and training for discrete action space -* [continuous_A3C.py](/continuous_A3C.py): Pendulum, neural net and training for continuous action space +* [shared_adam.py](/CARTPOLE/shared_adam.py): optimizer that shares its parameters in parallel +* [utils.py](/CARTPOLE/cart_utils.py): contains plots, probability distribution, optimizer and memory functions + -CartPole result -![cartpole](/results/cartpole.png) +CartPole results +![cartpole](/CARTPOLE/cart_results/Presentable_Results/Compared results/All.png) +![cartpole_test](/CARTPOLE/cart_results/Presentable_Results/Compared results/All_test.png) -Pendulum result +Vizdoom results ![pendulum](/results/pendulum.png) ## Dependencies From 0adf081f71b690ba2e63b1b800aff214fae79d99 Mon Sep 17 00:00:00 2001 From: Julien Look Date: Sun, 31 May 2020 12:30:38 +0200 Subject: [PATCH 57/63] VIZDOOM - push plots and plot data --- .../A2C-Sync/A2C_sync_comb_test.png | Bin 0 -> 32748 bytes .../A2C-Sync/A2C_sync_comb_test_raw.png | Bin 0 -> 41171 bytes .../doom_results/A2C-Sync/A2C_sync_test.png | Bin 0 -> 31958 bytes .../A2C-Sync/A2C_sync_test_raw.png | Bin 0 -> 39084 bytes VIZDOOM/doom_results/A2C/A2C_doom.png | Bin 0 -> 39332 bytes VIZDOOM/doom_results/A2C/A2C_doom_comb.png | Bin 0 -> 45872 bytes .../doom_results/A2C/A2C_doom_comb_raw.png | Bin 0 -> 42485 bytes .../doom_results/A2C/A2C_doom_comb_test.png | Bin 0 -> 42347 bytes .../A2C/A2C_doom_comb_test_raw.png | Bin 0 -> 44593 bytes VIZDOOM/doom_results/A2C/A2C_doom_raw.png | Bin 0 -> 42793 bytes VIZDOOM/doom_results/A2C/A2C_doom_test.png | Bin 0 -> 41518 bytes .../doom_results/A2C/A2C_doom_test_raw.png | Bin 0 -> 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