making the expert demonstration processor go through all agents in order when producing a single (default) trajectory file, using a randomized agent environment in optionsgail, starting function to process and store all expert data

scratch/arec/intersimple/gail_image_multiagent_nocollision.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 NRasterized
+
+from gail.discriminator import CnnDiscriminatorFlatAction
+
+model_name = 'gail_image_multiagent_nocollision'
+
+# %%
+# Load pickled test demonstrations.
+with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.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(NRasterized, n_envs=2, env_kwargs={'stop_on_collision':False, 'width': 36, 'height': 36, 'm_per_px': 2})
+
+tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
+tempdir_path = pathlib.Path(tempdir.name)
+print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
+
+# 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,
+    #n_disc_updates_per_round=2048,
+    discrim_kwargs={'discrim_net': CnnDiscriminatorFlatAction(venv)},
+    gen_algo=sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024),
+    allow_variable_horizon=True,
+)
+gail_trainer.train(total_timesteps=100000)
+gail_trainer.gen_algo.save(model_name)
+
+#del gail_trainer
+
+# %%
+model = sb3.PPO.load(model_name)
+
+env = NRasterized(stop_on_collision=False, width=36, height=36, m_per_px=2)
+
+obs = env.reset()
+while True:
+    action, _states = model.predict(obs)
+    obs, rewards, done, info = env.step(action)
+    env.render(mode='post')
+    if done:
+        break
+
+env.close(filestr='render/'+model_name)