#625 make copy of train_preference_comparisons.py for pebble

src/imitation/scripts/config/train_preference_comparisons_pebble.py

@@ -0,0 +1,128 @@
+"""Configuration for imitation.scripts.train_preference_comparisons_pebble."""
+
+import sacred
+
+from imitation.algorithms import preference_comparisons
+from imitation.scripts.common import common, reward, rl, train
+
+train_preference_comparisons_pebble_ex = sacred.Experiment(
+    "train_preference_comparisons_pebble",
+    ingredients=[
+        common.common_ingredient,
+        reward.reward_ingredient,
+        rl.rl_ingredient,
+        train.train_ingredient,
+    ],
+)
+
+
+MUJOCO_SHARED_LOCALS = dict(rl=dict(rl_kwargs=dict(ent_coef=0.1)))
+ANT_SHARED_LOCALS = dict(
+    total_timesteps=int(3e7),
+    rl=dict(batch_size=16384),
+)
+
+
+@train_preference_comparisons_pebble_ex.config
+def train_defaults():
+    fragment_length = 100  # timesteps per fragment used for comparisons
+    total_timesteps = int(1e6)  # total number of environment timesteps
+    total_comparisons = 5000  # total number of comparisons to elicit
+    num_iterations = 5  # Arbitrary, should be tuned for the task
+    comparison_queue_size = None
+    # factor by which to oversample transitions before creating fragments
+    transition_oversampling = 1
+    # fraction of total_comparisons that will be sampled right at the beginning
+    initial_comparison_frac = 0.1
+    # fraction of sampled trajectories that will include some random actions
+    exploration_frac = 0.0
+    preference_model_kwargs = {}
+    reward_trainer_kwargs = {
+        "epochs": 3,
+    }
+    save_preferences = False  # save preference dataset at the end?
+    agent_path = None  # path to a (partially) trained agent to load at the beginning
+    # type of PreferenceGatherer to use
+    gatherer_cls = preference_comparisons.SyntheticGatherer
+    # arguments passed on to the PreferenceGatherer specified by gatherer_cls
+    gatherer_kwargs = {}
+    active_selection = False
+    active_selection_oversampling = 2
+    uncertainty_on = "logit"
+    fragmenter_kwargs = {
+        "warning_threshold": 0,
+    }
+    # path to a pickled sequence of trajectories used instead of training an agent
+    trajectory_path = None
+    trajectory_generator_kwargs = {}  # kwargs to pass to trajectory generator
+    allow_variable_horizon = False
+
+    checkpoint_interval = 0  # Num epochs between saving (<0 disables, =0 final only)
+    query_schedule = "hyperbolic"
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def cartpole():
+    common = dict(env_name="CartPole-v1")
+    allow_variable_horizon = True
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def seals_ant():
+    locals().update(**MUJOCO_SHARED_LOCALS)
+    locals().update(**ANT_SHARED_LOCALS)
+    common = dict(env_name="seals/Ant-v0")
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def half_cheetah():
+    locals().update(**MUJOCO_SHARED_LOCALS)
+    common = dict(env_name="HalfCheetah-v2")
+    rl = dict(batch_size=16384, rl_kwargs=dict(batch_size=1024))
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def seals_hopper():
+    locals().update(**MUJOCO_SHARED_LOCALS)
+    common = dict(env_name="seals/Hopper-v0")
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def seals_humanoid():
+    locals().update(**MUJOCO_SHARED_LOCALS)
+    common = dict(env_name="seals/Humanoid-v0")
+    total_timesteps = int(4e6)
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def seals_cartpole():
+    common = dict(env_name="seals/CartPole-v0")
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def pendulum():
+    common = dict(env_name="Pendulum-v1")
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def mountain_car():
+    common = dict(env_name="MountainCar-v0")
+    allow_variable_horizon = True
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def seals_mountain_car():
+    common = dict(env_name="seals/MountainCar-v0")
+
+
+@train_preference_comparisons_pebble_ex.named_config
+def fast():
+    # Minimize the amount of computation. Useful for test cases.
+    total_timesteps = 50
+    total_comparisons = 5
+    initial_comparison_frac = 0.2
+    num_iterations = 1
+    fragment_length = 2
+    reward_trainer_kwargs = {
+        "epochs": 1,
+    }