80 lines
2.7 KiB
Python
80 lines
2.7 KiB
Python
# %%
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import pathlib
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import pickle
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import tempfile
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import stable_baselines3 as sb3
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from stable_baselines3.common.env_util import make_vec_env
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from imitation.algorithms import adversarial, bc
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from imitation.data import rollout
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from imitation.util import logger
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from intersim.envs.intersimple import NRasterizedRandomAgent, IntersimpleReward, speed_reward
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import functools
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from stable_baselines3.common.evaluation import evaluate_policy
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from gail.discriminator import CnnDiscriminator
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model_name = 'gail_image_random'
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# %%
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# Load pickled test demonstrations.
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with open("data/NormalizedIntersimpleExpertMu.001_NRasterizedRandomAgentw36h36mppx2.pkl", "rb") as f:
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# This is a list of `imitation.data.types.Trajectory`, where
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# every instance contains observations and actions for a single expert
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# demonstration.
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trajectories = pickle.load(f)
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# %%
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# Convert List[types.Trajectory] to an instance of `imitation.data.types.Transitions`.
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# This is a more general dataclass containing unordered
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# (observation, actions, next_observation) transitions.
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transitions = rollout.flatten_trajectories(trajectories)
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env_kwargs = {'width': 36, 'height': 36, 'm_per_px': 2}
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venv = make_vec_env(NRasterizedRandomAgent, n_envs=2, env_kwargs=env_kwargs)
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tempdir = tempfile.TemporaryDirectory(prefix="quickstart")
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tempdir_path = pathlib.Path(tempdir.name)
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print(f"All Tensorboards and logging are being written inside {tempdir_path}/.")
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# Train GAIL on expert data.
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# GAIL, and AIRL also accept as `expert_data` any Pytorch-style DataLoader that
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# iterates over dictionaries containing observations, actions, and next_observations.
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logger.configure(tempdir_path / "GAIL/")
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generator = sb3.PPO("CnnPolicy", venv, verbose=1, n_steps=1024)
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gail_trainer = adversarial.GAIL(
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venv,
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expert_data=transitions,
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expert_batch_size=32,
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#n_disc_updates_per_round=2048,
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discrim_kwargs={'discrim_net': CnnDiscriminator(venv)},
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gen_algo=generator,
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allow_variable_horizon=True,
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)
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def callback(round):
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eval_env = NRasterizedRandomAgent(reward=functools.partial(speed_reward, collision_penalty=0.), **env_kwargs)
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#sync_envs_normalization(self.training_env, self.eval_env)
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episode_rewards, episode_lengths = evaluate_policy(generator, eval_env, return_episode_rewards=True)
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gail_trainer.train(total_timesteps=100000, callback=callback)
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gail_trainer.gen_algo.save(model_name)
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#del gail_trainer
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# %%
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model = sb3.PPO.load(model_name)
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env = NRasterizedRandomAgent(width=36, height=36, m_per_px=2)
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obs = env.reset()
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while True:
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action, _states = model.predict(obs)
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obs, rewards, done, info = env.step(action)
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env.render(mode='post')
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if done:
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break
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env.close(filestr='render/'+model_name)
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