wehub-resource-sync
101 lines
3.4 KiB
Python
101 lines
3.4 KiB
Python
"""
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[1] Mastering Diverse Domains through World Models - 2023
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D. Hafner, J. Pasukonis, J. Ba, T. Lillicrap
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https://arxiv.org/pdf/2301.04104v1.pdf
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[2] Mastering Atari with Discrete World Models - 2021
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D. Hafner, T. Lillicrap, M. Norouzi, J. Ba
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https://arxiv.org/pdf/2010.02193.pdf
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"""
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# Run with:
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# python [this script name].py --env ale_py:ALE/[gym ID e.g. Pong-v5]
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# To see all available options:
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# python [this script name].py --help
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import gymnasium as gym
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from ray import tune
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from ray.rllib.algorithms.dreamerv3.dreamerv3 import DreamerV3Config
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from ray.rllib.env.wrappers.atari_wrappers import wrap_atari_for_new_api_stack
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from ray.rllib.examples.utils import (
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add_rllib_example_script_args,
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run_rllib_example_script_experiment,
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)
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parser = add_rllib_example_script_args(
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default_iters=1000000,
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default_reward=20.0,
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default_timesteps=100000,
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)
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parser.set_defaults(env="ale_py:ALE/Pong-v5")
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# Use `parser` to add your own custom command line options to this script
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# and (if needed) use their values to set up `config` below.
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args = parser.parse_args()
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# If we use >1 GPU and increase the batch size accordingly, we should also
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# increase the number of envs per worker.
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if args.num_envs_per_env_runner is None:
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args.num_envs_per_env_runner = args.num_learners or 1
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# Create the DreamerV3-typical Atari setup.
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def _env_creator(cfg):
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return wrap_atari_for_new_api_stack(
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gym.make(args.env, **cfg, render_mode="rgb_array"),
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# No framestacking necessary for Dreamer.
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framestack=None,
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# No grayscaling necessary for Dreamer.
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grayscale=False,
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)
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tune.register_env("env", _env_creator)
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default_config = DreamerV3Config()
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lr_multiplier = args.num_learners or 1
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config = (
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DreamerV3Config()
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.environment(
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env="env",
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# [2]: "We follow the evaluation protocol of Machado et al. (2018) with 200M
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# environment steps, action repeat of 4, a time limit of 108,000 steps per
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# episode that correspond to 30 minutes of game play, no access to life
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# information, full action space, and sticky actions. Because the world model
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# integrates information over time, DreamerV2 does not use frame stacking.
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# The experiments use a single-task setup where a separate agent is trained
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# for each game. Moreover, each agent uses only a single environment instance.
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env_config={
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# "sticky actions" but not according to Danijar's 100k configs.
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"repeat_action_probability": 0.0,
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# "full action space" but not according to Danijar's 100k configs.
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"full_action_space": False,
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# Already done by MaxAndSkip wrapper: "action repeat" == 4.
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"frameskip": 1,
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},
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)
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.env_runners(
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remote_worker_envs=(args.num_learners and args.num_learners > 1),
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)
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.reporting(
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metrics_num_episodes_for_smoothing=(args.num_learners or 1),
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report_images_and_videos=False,
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report_dream_data=False,
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report_individual_batch_item_stats=False,
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)
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# See Appendix A.
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.training(
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model_size="S",
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training_ratio=1024,
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batch_size_B=16 * (args.num_learners or 1),
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world_model_lr=default_config.world_model_lr * lr_multiplier,
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actor_lr=default_config.actor_lr * lr_multiplier,
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critic_lr=default_config.critic_lr * lr_multiplier,
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)
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)
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if __name__ == "__main__":
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run_rllib_example_script_experiment(config, args)
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