6cbee188b8
of number of environments in SamplingConfig is used (values are now passed to factory method) This is clearer and removes the need to pass otherwise unnecessary configuration to environment factories at construction
88 lines
2.6 KiB
Python
88 lines
2.6 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 typing import Literal
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from jsonargparse import CLI
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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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REDQExperimentBuilder,
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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 REDQParams
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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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ensemble_size: int = 10,
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subset_size: int = 2,
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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 = 20,
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n_step: int = 1,
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batch_size: int = 256,
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target_mode: Literal["mean", "min"] = "min",
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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, "redq", 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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batch_size=batch_size,
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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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step_per_collect=step_per_collect,
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update_per_step=update_per_step,
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repeat_per_collect=None,
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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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REDQExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_redq_params(
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REDQParams(
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actor_lr=actor_lr,
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critic_lr=critic_lr,
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gamma=gamma,
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tau=tau,
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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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target_mode=target_mode,
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subset_size=subset_size,
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ensemble_size=ensemble_size,
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),
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)
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.with_actor_factory_default(hidden_sizes)
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.with_critic_ensemble_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_main(lambda: CLI(main))
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