83 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			83 lines
		
	
	
		
			2.4 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
#!/usr/bin/env python3
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import os
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from collections.abc import Sequence
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from examples.mujoco.mujoco_env import MujocoEnvFactory
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from tianshou.highlevel.config import SamplingConfig
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from tianshou.highlevel.experiment import (
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    ExperimentConfig,
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    SACExperimentBuilder,
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)
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from tianshou.highlevel.params.alpha import AutoAlphaFactoryDefault
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from tianshou.highlevel.params.policy_params import SACParams
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from tianshou.utils import logging
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from tianshou.utils.logging import datetime_tag
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def main(
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    experiment_config: ExperimentConfig,
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    task: str = "Ant-v3",
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    buffer_size: int = 1000000,
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    hidden_sizes: Sequence[int] = (256, 256),
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    actor_lr: float = 1e-3,
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    critic_lr: float = 1e-3,
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    gamma: float = 0.99,
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    tau: float = 0.005,
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    alpha: float = 0.2,
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    auto_alpha: bool = False,
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    alpha_lr: float = 3e-4,
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    start_timesteps: int = 10000,
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    epoch: int = 200,
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    step_per_epoch: int = 5000,
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    step_per_collect: int = 1,
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    update_per_step: int = 1,
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    n_step: int = 1,
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    batch_size: int = 256,
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    training_num: int = 1,
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    test_num: int = 10,
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):
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    log_name = os.path.join(task, "sac", str(experiment_config.seed), datetime_tag())
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    sampling_config = SamplingConfig(
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        num_epochs=epoch,
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        step_per_epoch=step_per_epoch,
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        num_train_envs=training_num,
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        num_test_envs=test_num,
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        buffer_size=buffer_size,
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        batch_size=batch_size,
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        step_per_collect=step_per_collect,
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        update_per_step=update_per_step,
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        start_timesteps=start_timesteps,
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        start_timesteps_random=True,
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    )
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    env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=False)
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    experiment = (
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        SACExperimentBuilder(env_factory, experiment_config, sampling_config)
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        .with_sac_params(
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            SACParams(
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                tau=tau,
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                gamma=gamma,
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                alpha=AutoAlphaFactoryDefault(lr=alpha_lr) if auto_alpha else alpha,
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                estimation_step=n_step,
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                actor_lr=actor_lr,
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                critic1_lr=critic_lr,
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                critic2_lr=critic_lr,
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            ),
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        )
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        .with_actor_factory_default(
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            hidden_sizes,
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            continuous_unbounded=True,
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            continuous_conditioned_sigma=True,
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        )
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        .with_common_critic_factory_default(hidden_sizes)
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        .build()
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    )
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    experiment.run(log_name)
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if __name__ == "__main__":
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    logging.run_cli(main)
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