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scratch/etienne/intersimple/imitation_quickstart.py

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+# %%
+import pathlib
+import pickle
+import tempfile
+
+import stable_baselines3 as sb3
+from stable_baselines3.common.env_util import make_vec_env
+
+from imitation.algorithms import adversarial, bc
+from imitation.data import rollout
+from imitation.util import logger
+
+from intersim.envs.intersimple import IntersimpleReward
+
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpert_IntersimpleRewardAgent51.pkl", "rb") as f:
+    # This is a list of `imitation.data.types.Trajectory`, where
+    # every instance contains observations and actions for a single expert
+    # demonstration.
+    trajectories = pickle.load(f)
+
+# %%
+# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
+# This is a more general dataclass containing unordered
+# (observation, actions, next_observation) transitions.
+transitions = rollout.flatten_trajectories(trajectories)
+
+venv = make_vec_env(IntersimpleReward, n_envs=2, env_kwargs={'agent': 51})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# Train BC on expert data.
+# BC also accepts as `expert_data` any PyTorch-style DataLoader that iterates over
+# dictionaries containing observations and actions.
+logger.configure(tempdir_path / "BC/")
+bc_trainer = bc.BC(venv.observation_space, venv.action_space, expert_data=transitions)
+bc_trainer.train(n_epochs=1)
+
+# Train GAIL on expert data.
+# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
+# iterates over dictionaries containing observations, actions, and next_observations.
+logger.configure(tempdir_path / "GAIL/")
+gail_trainer = adversarial.GAIL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=1024),
+)
+gail_trainer.train(total_timesteps=2048)
+
+# Train AIRL on expert data.
+logger.configure(tempdir_path / "AIRL/")
+airl_trainer = adversarial.AIRL(
+    venv,
+    expert_data=transitions,
+    expert_batch_size=32,
+    gen_algo=sb3.PPO("MlpPolicy", venv, verbose=1, n_steps=1024),
+)
+airl_trainer.train(total_timesteps=2048)
+
+# %%