Tianshou/test/modelbased/test_psrl.py

import argparse
import os
import pprint

import numpy as np
import pytest
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.policy import PSRLPolicy
from tianshou.trainer import OnpolicyTrainer
from tianshou.utils import LazyLogger, TensorboardLogger, WandbLogger

try:
    import envpool
except ImportError:
    envpool = None


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="NChain-v0")
    parser.add_argument("--reward-threshold", type=float, default=None)
    parser.add_argument("--seed", type=int, default=1)
    parser.add_argument("--buffer-size", type=int, default=50000)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=1000)
    parser.add_argument("--episode-per-collect", type=int, default=1)
    parser.add_argument("--training-num", type=int, default=1)
    parser.add_argument("--test-num", type=int, default=10)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.0)
    parser.add_argument("--rew-mean-prior", type=float, default=0.0)
    parser.add_argument("--rew-std-prior", type=float, default=1.0)
    parser.add_argument("--gamma", type=float, default=0.99)
    parser.add_argument("--eps", type=float, default=0.01)
    parser.add_argument("--add-done-loop", action="store_true", default=False)
    parser.add_argument(
        "--logger",
        type=str,
        default="none",  # TODO: Change to "wandb" once wandb supports Gym >=0.26.0
        choices=["wandb", "tensorboard", "none"],
    )
    return parser.parse_known_args()[0]


@pytest.mark.skipif(envpool is None, reason="EnvPool doesn't support this platform")
def test_psrl(args: argparse.Namespace = get_args()) -> None:
    # if you want to use python vector env, please refer to other test scripts
    train_envs = env = envpool.make_gymnasium(args.task, num_envs=args.training_num, seed=args.seed)
    test_envs = envpool.make_gymnasium(args.task, num_envs=args.test_num, seed=args.seed)
    if args.reward_threshold is None:
        default_reward_threshold = {"NChain-v0": 3400}
        args.reward_threshold = default_reward_threshold.get(args.task, env.spec.reward_threshold)
    print("reward threshold:", args.reward_threshold)
    args.state_shape = env.observation_space.shape or env.observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    # model
    n_action = args.action_shape
    n_state = args.state_shape
    trans_count_prior = np.ones((n_state, n_action, n_state))
    rew_mean_prior = np.full((n_state, n_action), args.rew_mean_prior)
    rew_std_prior = np.full((n_state, n_action), args.rew_std_prior)
    policy: PSRLPolicy = PSRLPolicy(
        trans_count_prior=trans_count_prior,
        rew_mean_prior=rew_mean_prior,
        rew_std_prior=rew_std_prior,
        action_space=env.action_space,
        discount_factor=args.gamma,
        epsilon=args.eps,
        add_done_loop=args.add_done_loop,
    )
    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        exploration_noise=True,
    )
    train_collector.reset()
    test_collector = Collector(policy, test_envs)
    test_collector.reset()
    # Logger
    log_path = os.path.join(args.logdir, args.task, "psrl")
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger: WandbLogger | TensorboardLogger | LazyLogger
    if args.logger == "wandb":
        logger = WandbLogger(save_interval=1, project="psrl", name="wandb_test", config=args)
        logger.load(writer)
    elif args.logger == "tensorboard":
        logger = TensorboardLogger(writer)
    else:
        logger = LazyLogger()

    def stop_fn(mean_rewards: float) -> bool:
        return mean_rewards >= args.reward_threshold

    train_collector.collect(n_step=args.buffer_size, random=True)
    # trainer, test it without logger
    result = OnpolicyTrainer(
        policy=policy,
        train_collector=train_collector,
        test_collector=test_collector,
        max_epoch=args.epoch,
        step_per_epoch=args.step_per_epoch,
        repeat_per_collect=1,
        episode_per_test=args.test_num,
        batch_size=0,
        episode_per_collect=args.episode_per_collect,
        stop_fn=stop_fn,
        logger=logger,
        test_in_train=False,
    ).run()

    if __name__ == "__main__":
        pprint.pprint(result)
        # Let's watch its performance!
        policy.eval()
        test_envs.seed(args.seed)
        result = test_collector.collect(n_episode=args.test_num, render=args.render)
        print(f"Final reward: {result.rew_mean}, length: {result.len_mean}")
    elif env.spec.reward_threshold:
        assert result.best_reward >= env.spec.reward_threshold


if __name__ == "__main__":
    test_psrl()