Tianshou/test/pettingzoo/pistonball.py

import argparse
import os
import warnings
from typing import List, Optional, Tuple

import gymnasium as gym
import numpy as np
import pettingzoo.butterfly.pistonball_v6 as pistonball_v6
import torch
from torch.utils.tensorboard import SummaryWriter

from tianshou.data import Collector, VectorReplayBuffer
from tianshou.env import DummyVectorEnv
from tianshou.env.pettingzoo_env import PettingZooEnv
from tianshou.policy import BasePolicy, DQNPolicy, MultiAgentPolicyManager
from tianshou.trainer import offpolicy_trainer
from tianshou.utils import TensorboardLogger
from tianshou.utils.net.common import Net


def get_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser()
    parser.add_argument('--seed', type=int, default=1626)
    parser.add_argument('--eps-test', type=float, default=0.05)
    parser.add_argument('--eps-train', type=float, default=0.1)
    parser.add_argument('--buffer-size', type=int, default=2000)
    parser.add_argument('--lr', type=float, default=1e-3)
    parser.add_argument(
        '--gamma', type=float, default=0.9, help='a smaller gamma favors earlier win'
    )
    parser.add_argument(
        '--n-pistons',
        type=int,
        default=3,
        help='Number of pistons(agents) in the env'
    )
    parser.add_argument('--n-step', type=int, default=100)
    parser.add_argument('--target-update-freq', type=int, default=320)
    parser.add_argument('--epoch', type=int, default=3)
    parser.add_argument('--step-per-epoch', type=int, default=500)
    parser.add_argument('--step-per-collect', type=int, default=10)
    parser.add_argument('--update-per-step', type=float, default=0.1)
    parser.add_argument('--batch-size', type=int, default=100)
    parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64])
    parser.add_argument('--training-num', type=int, default=10)
    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(
        '--watch',
        default=False,
        action='store_true',
        help='no training, '
        'watch the play of pre-trained models'
    )
    parser.add_argument(
        '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
    )
    return parser


def get_args() -> argparse.Namespace:
    parser = get_parser()
    return parser.parse_known_args()[0]


def get_env(args: argparse.Namespace = get_args()):
    return PettingZooEnv(pistonball_v6.env(continuous=False, n_pistons=args.n_pistons))


def get_agents(
    args: argparse.Namespace = get_args(),
    agents: Optional[List[BasePolicy]] = None,
    optims: Optional[List[torch.optim.Optimizer]] = None,
) -> Tuple[BasePolicy, List[torch.optim.Optimizer], List]:
    env = get_env()
    observation_space = env.observation_space['observation'] if isinstance(
        env.observation_space, gym.spaces.Dict
    ) else env.observation_space
    args.state_shape = observation_space.shape or observation_space.n
    args.action_shape = env.action_space.shape or env.action_space.n
    if agents is None:
        agents = []
        optims = []
        for _ in range(args.n_pistons):
            # model
            net = Net(
                args.state_shape,
                args.action_shape,
                hidden_sizes=args.hidden_sizes,
                device=args.device
            ).to(args.device)
            optim = torch.optim.Adam(net.parameters(), lr=args.lr)
            agent = DQNPolicy(
                net,
                optim,
                args.gamma,
                args.n_step,
                target_update_freq=args.target_update_freq
            )
            agents.append(agent)
            optims.append(optim)

    policy = MultiAgentPolicyManager(agents, env)
    return policy, optims, env.agents


def train_agent(
    args: argparse.Namespace = get_args(),
    agents: Optional[List[BasePolicy]] = None,
    optims: Optional[List[torch.optim.Optimizer]] = None,
) -> Tuple[dict, BasePolicy]:
    train_envs = DummyVectorEnv([get_env for _ in range(args.training_num)])
    test_envs = DummyVectorEnv([get_env 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)

    policy, optim, agents = get_agents(args, agents=agents, optims=optims)

    # collector
    train_collector = Collector(
        policy,
        train_envs,
        VectorReplayBuffer(args.buffer_size, len(train_envs)),
        exploration_noise=True
    )
    test_collector = Collector(policy, test_envs, exploration_noise=True)
    train_collector.collect(n_step=args.batch_size * args.training_num)
    # log
    log_path = os.path.join(args.logdir, 'pistonball', 'dqn')
    writer = SummaryWriter(log_path)
    writer.add_text("args", str(args))
    logger = TensorboardLogger(writer)

    def save_best_fn(policy):
        pass

    def stop_fn(mean_rewards):
        return False

    def train_fn(epoch, env_step):
        [agent.set_eps(args.eps_train) for agent in policy.policies.values()]

    def test_fn(epoch, env_step):
        [agent.set_eps(args.eps_test) for agent in policy.policies.values()]

    def reward_metric(rews):
        return rews[:, 0]

    # trainer
    result = offpolicy_trainer(
        policy,
        train_collector,
        test_collector,
        args.epoch,
        args.step_per_epoch,
        args.step_per_collect,
        args.test_num,
        args.batch_size,
        train_fn=train_fn,
        test_fn=test_fn,
        stop_fn=stop_fn,
        save_best_fn=save_best_fn,
        update_per_step=args.update_per_step,
        logger=logger,
        test_in_train=False,
        reward_metric=reward_metric
    )

    return result, policy


def watch(
    args: argparse.Namespace = get_args(), policy: Optional[BasePolicy] = None
) -> None:
    env = DummyVectorEnv([get_env])
    if not policy:
        warnings.warn(
            "watching random agents, as loading pre-trained policies is "
            "currently not supported"
        )
        policy, _, _ = get_agents(args)
    policy.eval()
    [agent.set_eps(args.eps_test) for agent in policy.policies.values()]
    collector = Collector(policy, env, exploration_noise=True)
    result = collector.collect(n_episode=1, render=args.render)
    rews, lens = result["rews"], result["lens"]
    print(f"Final reward: {rews[:, 0].mean()}, length: {lens.mean()}")