test.py

import argparse
import os

import gymnasium as gym
import numpy as np
import torch
from torch.distributions import Distribution, Independent, Normal
from torch.utils.tensorboard import SummaryWriter

from data import Collector, CollectStats, VectorReplayBuffer
from env import DummyVectorEnv
from policy_set import PPOPolicy
from policy_set.base import BasePolicy
from policy_set.ppo import PPOTrainingStats
from trainer import OnpolicyTrainer
from utils import TensorboardLogger
from utils.net.common import ActorCritic, Net
from utils.net.continuous import ActorProb, Critic
from utils.space_info import SpaceInfo


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--task", type=str, default="Pendulum-v1")
    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=20000)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--gamma", type=float, default=0.95)
    parser.add_argument("--epoch", type=int, default=5)
    parser.add_argument("--step-per-epoch", type=int, default=150000)
    parser.add_argument("--episode-per-collect", type=int, default=16)
    parser.add_argument("--repeat-per-collect", type=int, default=2)
    parser.add_argument("--batch-size", type=int, default=128)
    parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
    parser.add_argument("--training-num", type=int, default=16)
    parser.add_argument("--test-num", type=int, default=100)
    parser.add_argument("--logdir", type=str, default="log")
    parser.add_argument("--render", type=float, default=0.0)
    parser.add_argument(
        "--device",
        type=str,
        default="cuda" if torch.cuda.is_available() else "cpu",
    )
    # ppo special
    parser.add_argument("--vf-coef", type=float, default=0.25)
    parser.add_argument("--ent-coef", type=float, default=0.0)
    parser.add_argument("--eps-clip", type=float, default=0.2)
    parser.add_argument("--max-grad-norm", type=float, default=0.5)
    parser.add_argument("--gae-lambda", type=float, default=0.95)
    parser.add_argument("--rew-norm", type=int, default=1)
    parser.add_argument("--dual-clip", type=float, default=None)
    parser.add_argument("--value-clip", type=int, default=1)
    parser.add_argument("--norm-adv", type=int, default=1)
    parser.add_argument("--recompute-adv", type=int, default=0)
    parser.add_argument("--resume", action="store_true")
    parser.add_argument("--save-interval", type=int, default=4)
    return parser.parse_known_args()[0]


def test_ppo(args: argparse.Namespace = get_args()) -> None:
    env = gym.make(args.task)

    space_info = SpaceInfo.from_env(env)
    args.state_shape = space_info.observation_info.obs_shape
    args.action_shape = space_info.action_info.action_shape
    args.max_action = space_info.action_info.max_action

    if args.reward_threshold is None:
        default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250}
        args.reward_threshold = default_reward_threshold.get(
            args.task,
            env.spec.reward_threshold if env.spec else None,
        )
    # you can also use tianshou.env.SubprocVectorEnv
    # train_envs = gym.make(args.task)
    train_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.training_num)])
    # test_envs = gym.make(args.task)
    test_envs = DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)])
    # seed
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    train_envs.seed(args.seed)
    test_envs.seed(args.seed)
    # model
    net = Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
    actor = ActorProb(net, args.action_shape, unbounded=True, device=args.device).to(args.device)
    critic = Critic(
        Net(state_shape=args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device),
        device=args.device,
    ).to(args.device)
    actor_critic = ActorCritic(actor, critic)
    # orthogonal initialization
    for m in actor_critic.modules():
        if isinstance(m, torch.nn.Linear):
            torch.nn.init.orthogonal_(m.weight)
            torch.nn.init.zeros_(m.bias)
    optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr)

    # replace DiagGuassian with Independent(Normal) which is equivalent
    # pass *logits to be consistent with policy.forward
    def dist(loc_scale: tuple[torch.Tensor, torch.Tensor]) -> Distribution:
        loc, scale = loc_scale
        return Independent(Normal(loc, scale), 1)

    policy: PPOPolicy[PPOTrainingStats] = PPOPolicy(
        actor=actor,
        critic=critic,
        optim=optim,
        dist_fn=dist,
        discount_factor=args.gamma,
        max_grad_norm=args.max_grad_norm,
        eps_clip=args.eps_clip,
        vf_coef=args.vf_coef,
        ent_coef=args.ent_coef,
        reward_normalization=args.rew_norm,
        advantage_normalization=args.norm_adv,
        recompute_advantage=args.recompute_adv,
        dual_clip=args.dual_clip,
        value_clip=args.value_clip,
        gae_lambda=args.gae_lambda,
        action_space=env.action_space,
    )
    # collector
    train_collector = Collector[CollectStats](
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
    )
    test_collector = Collector[CollectStats](policy, test_envs)
    # log
    log_path = os.path.join(args.logdir, args.task, "ppo")
    writer = SummaryWriter(log_path)
    logger = TensorboardLogger(writer, save_interval=args.save_interval)

    def save_best_fn(policy: BasePolicy) -> None:
        torch.save(policy.state_dict(), os.path.join(log_path, "policy.pth"))

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

    def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str:
        # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html
        ckpt_path = os.path.join(log_path, "checkpoint.pth")
        # Example: saving by epoch num
        # ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth")
        torch.save(
            {
                "model": policy.state_dict(),
                "optim": optim.state_dict(),
            },
            ckpt_path,
        )
        return ckpt_path

    if args.resume:
        # load from existing checkpoint
        print(f"Loading agent under {log_path}")
        ckpt_path = os.path.join(log_path, "checkpoint.pth")
        if os.path.exists(ckpt_path):
            checkpoint = torch.load(ckpt_path, map_location=args.device)
            policy.load_state_dict(checkpoint["model"])
            optim.load_state_dict(checkpoint["optim"])
            print("Successfully restore policy and optim.")
        else:
            print("Fail to restore policy and optim.")

    # trainer
    trainer = 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=args.repeat_per_collect,
        episode_per_test=args.test_num,
        batch_size=args.batch_size,
        episode_per_collect=args.episode_per_collect,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        logger=logger,
        resume_from_log=args.resume,
        save_checkpoint_fn=save_checkpoint_fn,
    )

    for epoch_stat in trainer:
        print(f"Epoch: {epoch_stat.epoch}")
        print(epoch_stat)
        # print(info)

    assert stop_fn(epoch_stat.info_stat.best_reward)


def test_ppo_resume(args: argparse.Namespace = get_args()) -> None:
    args.resume = True
    test_ppo(args)