wehub-resource-sync
65 lines
1.9 KiB
Python
65 lines
1.9 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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from ray.rllib.algorithms.dreamerv3.dreamerv3 import DreamerV3Config
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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=10000,
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default_reward=-200.0,
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default_timesteps=100000,
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)
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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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# Run with:
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# python [this script name].py
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# To see all available options:
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# python [this script name].py --help
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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("Pendulum-v1")
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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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