Tianshou/examples/mujoco/mujoco_reinforce_hl.py

#!/usr/bin/env python3

import functools
import os
from collections.abc import Sequence
from typing import Literal

import torch

from examples.mujoco.mujoco_env import MujocoEnvFactory
from tianshou.highlevel.config import SamplingConfig
from tianshou.highlevel.experiment import (
    ExperimentConfig,
    PGExperimentBuilder,
)
from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
from tianshou.highlevel.params.policy_params import PGParams
from tianshou.utils import logging
from tianshou.utils.logging import datetime_tag


def main(
    experiment_config: ExperimentConfig,
    task: str = "Ant-v4",
    buffer_size: int = 4096,
    hidden_sizes: Sequence[int] = (64, 64),
    lr: float = 1e-3,
    gamma: float = 0.99,
    epoch: int = 100,
    step_per_epoch: int = 30000,
    step_per_collect: int = 2048,
    repeat_per_collect: int = 1,
    batch_size: int | None = None,
    training_num: int = 10,
    test_num: int = 10,
    rew_norm: bool = True,
    action_bound_method: Literal["clip", "tanh"] = "tanh",
    lr_decay: bool = True,
) -> None:
    log_name = os.path.join(task, "reinforce", str(experiment_config.seed), datetime_tag())

    sampling_config = SamplingConfig(
        num_epochs=epoch,
        step_per_epoch=step_per_epoch,
        batch_size=batch_size,
        num_train_envs=training_num,
        num_test_envs=test_num,
        buffer_size=buffer_size,
        step_per_collect=step_per_collect,
        repeat_per_collect=repeat_per_collect,
    )

    env_factory = MujocoEnvFactory(task, experiment_config.seed, obs_norm=True)

    experiment = (
        PGExperimentBuilder(env_factory, experiment_config, sampling_config)
        .with_pg_params(
            PGParams(
                discount_factor=gamma,
                action_bound_method=action_bound_method,
                reward_normalization=rew_norm,
                lr=lr,
                lr_scheduler_factory=LRSchedulerFactoryLinear(sampling_config)
                if lr_decay
                else None,
            ),
        )
        .with_actor_factory_default(hidden_sizes, torch.nn.Tanh, continuous_unbounded=True)
        .build()
    )
    experiment.run(log_name)


if __name__ == "__main__":
    run_with_default_config = functools.partial(main, experiment_config=ExperimentConfig())
    logging.run_cli(run_with_default_config)