RL Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adapted from: https://keon.io/deep-q-learning/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "import gym\n",
    "import numpy as np\n",
    "from collections import deque\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deep Q-learning Agent\n",
    "class DQNAgent:\n",
    "    def __init__(self, state_size, action_size):\n",
    "        self.state_size = state_size\n",
    "        self.action_size = action_size\n",
    "        self.memory = deque(maxlen=2000)\n",
    "        self.gamma = 0.95    # discount rate\n",
    "        self.epsilon = 1.0  # exploration rate\n",
    "        self.epsilon_min = 0.01\n",
    "        self.epsilon_decay = 0.995\n",
    "        self.learning_rate = 0.001\n",
    "        self.model = self._build_model()\n",
    "        \n",
    "    def _build_model(self):\n",
    "        # Neural Net for Deep-Q learning Model\n",
    "        model = Sequential()\n",
    "        model.add(Dense(24, input_dim=self.state_size, activation='relu'))\n",
    "        model.add(Dense(24, activation='relu'))\n",
    "        model.add(Dense(self.action_size, activation='linear'))\n",
    "        model.compile(loss='mse',\n",
    "                      optimizer=Adam(lr=self.learning_rate))\n",
    "        return model\n",
    "    def remember(self, state, action, reward, next_state, done):\n",
    "        self.memory.append((state, action, reward, next_state, done))\n",
    "    def act(self, state):\n",
    "        if np.random.rand() <= self.epsilon:\n",
    "            return random.randrange(self.action_size)\n",
    "        act_values = self.model.predict(state)\n",
    "        return np.argmax(act_values[0])  # returns action\n",
    "    def replay(self, batch_size):\n",
    "        minibatch = random.sample(self.memory, batch_size)\n",
    "        for state, action, reward, next_state, done in minibatch:\n",
    "            target = reward\n",
    "            if not done:\n",
    "              target = reward + self.gamma * \\\n",
    "                       np.amax(self.model.predict(next_state)[0]) # discounted\n",
    "            target_f = self.model.predict(state)\n",
    "            target_f[0][action] = target\n",
    "            self.model.fit(state, target_f, epochs=1, verbose=0)\n",
    "        if self.epsilon > self.epsilon_min:\n",
    "            self.epsilon *= self.epsilon_decay\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 10:17:20,066] Making new env: CartPole-v0\n",
      "[2018-05-09 10:17:20,083] Clearing 2175 monitor files from previous run (because force=True was provided)\n"
     ]
    }
   ],
   "source": [
    "env = gym.make('CartPole-v0')\n",
    "env._max_episodes=5000\n",
    "EPISODES = 1000\n",
    "env = gym.wrappers.Monitor(env, directory='/tmp', video_callable=lambda episode_id: True, force=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_size = env.observation_space.shape[0]\n",
    "action_size = env.action_space.n\n",
    "agent = DQNAgent(state_size, action_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "done = False\n",
    "batch_size = 32\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 10:17:22,480] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000000.mp4\n",
      "[2018-05-09 10:17:23,767] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000001.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 0/1000, score: 11, e: 1.0\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[2018-05-09 10:17:24,056] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000002.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 1/1000, score: 13, e: 1.0\n",
      "episode: 2/1000, score: 14, e: 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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    },
    {
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     "text": [
      "episode: 3/1000, score: 13, e: 0.99\n"
     ]
    },
    {
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      "episode: 4/1000, score: 38, e: 0.99\n"
     ]
    },
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    {
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     "text": [
      "episode: 5/1000, score: 12, e: 0.99\n"
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     "text": [
      "episode: 6/1000, score: 36, e: 0.98\n"
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     "text": [
      "episode: 7/1000, score: 9, e: 0.98\n"
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    },
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      "episode: 8/1000, score: 9, e: 0.97\n"
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      "episode: 9/1000, score: 27, e: 0.97\n"
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      "[2018-05-09 10:17:29,264] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000011.mp4\n"
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     "text": [
      "episode: 10/1000, score: 17, e: 0.96\n"
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      "episode: 11/1000, score: 13, e: 0.96\n"
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      "episode: 12/1000, score: 23, e: 0.95\n"
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      "episode: 13/1000, score: 62, e: 0.95\n"
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      "episode: 14/1000, score: 30, e: 0.94\n"
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      "episode: 15/1000, score: 13, e: 0.94\n"
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      "episode: 16/1000, score: 23, e: 0.93\n"
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      "episode: 17/1000, score: 18, e: 0.93\n"
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      "episode: 18/1000, score: 52, e: 0.92\n"
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      "episode: 20/1000, score: 11, e: 0.91\n"
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      "episode: 28/1000, score: 10, e: 0.88\n"
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      "episode: 29/1000, score: 21, e: 0.87\n"
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      "episode: 31/1000, score: 18, e: 0.86\n"
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      "episode: 493/1000, score: 199, e: 0.085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:18,213] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000495.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 494/1000, score: 121, e: 0.085\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:21,264] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000496.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 495/1000, score: 190, e: 0.084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:22,914] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000497.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 496/1000, score: 116, e: 0.084\n",
      "episode: 497/1000, score: 136, e: 0.084\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:24,723] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000498.mp4\n",
      "[2018-05-09 11:00:25,943] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000499.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 498/1000, score: 88, e: 0.083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:27,167] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000500.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 499/1000, score: 101, e: 0.083\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2018-05-09 11:00:28,558] Starting new video recorder writing to /tmp/openaigym.video.0.27601.video000501.mp4\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "episode: 500/1000, score: 119, e: 0.082\n"
     ]
    }
   ],
   "source": [
    "for e in range(EPISODES):\n",
    "    state = env.reset()\n",
    "    state = np.reshape(state, [1, state_size])\n",
    "    for time in range(500):\n",
    "        # env.render()\n",
    "        action = agent.act(state)\n",
    "        next_state, reward, done, _ = env.step(action)\n",
    "        reward = reward if not done else -10\n",
    "        next_state = np.reshape(next_state, [1, state_size])\n",
    "        agent.remember(state, action, reward, next_state, done)\n",
    "        state = next_state\n",
    "        if done:\n",
    "            print(\"episode: {}/{}, score: {}, e: {:.2}\"\n",
    "                  .format(e, EPISODES, time, agent.epsilon))\n",
    "            break\n",
    "    if len(agent.memory) > batch_size:\n",
    "        agent.replay(batch_size)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!which python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}