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
89 lines
2.6 KiB
Python
89 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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import torch
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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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TD3ExperimentBuilder,
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)
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from tianshou.highlevel.params.env_param import MaxActionScaled
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from tianshou.highlevel.params.noise import (
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MaxActionScaledGaussian,
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)
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from tianshou.highlevel.params.policy_params import TD3Params
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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 = 3e-4,
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critic_lr: float = 3e-4,
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gamma: float = 0.99,
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tau: float = 0.005,
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exploration_noise: float = 0.1,
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policy_noise: float = 0.2,
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noise_clip: float = 0.5,
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update_actor_freq: int = 2,
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start_timesteps: int = 25000,
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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, "td3", 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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TD3ExperimentBuilder(env_factory, experiment_config, sampling_config)
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.with_td3_params(
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TD3Params(
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tau=tau,
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gamma=gamma,
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estimation_step=n_step,
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update_actor_freq=update_actor_freq,
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noise_clip=MaxActionScaled(noise_clip),
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policy_noise=MaxActionScaled(policy_noise),
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exploration_noise=MaxActionScaledGaussian(exploration_noise),
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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(hidden_sizes, torch.nn.Tanh)
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.with_common_critic_factory_default(hidden_sizes, torch.nn.Tanh)
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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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