mujoco_trpo_hl.py

#!/usr/bin/env python3

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

import torch
from sensai.util import logging
from sensai.util.logging import datetime_tag

from examples.mujoco.mujoco_env import MujocoEnvFactory
from tianshou.highlevel.config import SamplingConfig
from tianshou.highlevel.experiment import (
    ExperimentConfig,
    TRPOExperimentBuilder,
)
from tianshou.highlevel.params.lr_scheduler import LRSchedulerFactoryLinear
from tianshou.highlevel.params.policy_params import TRPOParams


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 = 1024,
    repeat_per_collect: int = 1,
    batch_size: int = 16,
    training_num: int = 16,
    test_num: int = 10,
    rew_norm: bool = True,
    gae_lambda: float = 0.95,
    bound_action_method: Literal["clip", "tanh"] = "clip",
    lr_decay: bool = True,
    norm_adv: bool = True,
    optim_critic_iters: int = 20,
    max_kl: float = 0.01,
    backtrack_coeff: float = 0.8,
    max_backtracks: int = 10,
) -> None:
    log_name = os.path.join(task, "trpo", 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,
        train_seed=sampling_config.train_seed,
        test_seed=sampling_config.test_seed,
        obs_norm=True,
    )

    experiment = (
        TRPOExperimentBuilder(env_factory, experiment_config, sampling_config)
        .with_trpo_params(
            TRPOParams(
                discount_factor=gamma,
                gae_lambda=gae_lambda,
                action_bound_method=bound_action_method,
                reward_normalization=rew_norm,
                advantage_normalization=norm_adv,
                optim_critic_iters=optim_critic_iters,
                max_kl=max_kl,
                backtrack_coeff=backtrack_coeff,
                max_backtracks=max_backtracks,
                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)
        .with_critic_factory_default(hidden_sizes, torch.nn.Tanh)
        .build()
    )
    experiment.run(run_name=log_name)


if __name__ == "__main__":
    logging.run_cli(main)