49781e715e
The high-level examples were all broken by changes made to make mypy pass. This PR fixes them, making a type change in logging.run_cli instead to make mypy happy.
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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import torch
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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-v4",
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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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) -> None:
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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_cli(main)
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