import argparse import os from copy import deepcopy from functools import partial import gymnasium import numpy as np import torch from pettingzoo.classic import tictactoe_v3 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, RandomPolicy from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import Net def get_env(render_mode: str | None = None): return PettingZooEnv(tictactoe_v3.env(render_mode=render_mode)) 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=20000) parser.add_argument("--lr", type=float, default=1e-4) parser.add_argument( "--gamma", type=float, default=0.9, help="a smaller gamma favors earlier win", ) parser.add_argument("--n-step", type=int, default=3) parser.add_argument("--target-update-freq", type=int, default=320) parser.add_argument("--epoch", type=int, default=50) parser.add_argument("--step-per-epoch", type=int, default=1000) 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=64) parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[128, 128, 128, 128]) 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=0.1) parser.add_argument( "--win-rate", type=float, default=0.6, help="the expected winning rate: Optimal policy can get 0.7", ) parser.add_argument( "--watch", default=False, action="store_true", help="no training, watch the play of pre-trained models", ) parser.add_argument( "--agent-id", type=int, default=2, help="the learned agent plays as the agent_id-th player. Choices are 1 and 2.", ) parser.add_argument( "--resume-path", type=str, default="", help="the path of agent pth file for resuming from a pre-trained agent", ) parser.add_argument( "--opponent-path", type=str, default="", help="the path of opponent agent pth file for resuming from a pre-trained agent", ) 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_agents( args: argparse.Namespace = get_args(), agent_learn: BasePolicy | None = None, agent_opponent: BasePolicy | None = None, optim: torch.optim.Optimizer | None = None, ) -> tuple[BasePolicy, torch.optim.Optimizer, list]: env = get_env() observation_space = ( env.observation_space["observation"] if isinstance(env.observation_space, gymnasium.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 agent_learn is None: # model net = Net( args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device, ).to(args.device) if optim is None: optim = torch.optim.Adam(net.parameters(), lr=args.lr) agent_learn = DQNPolicy( model=net, optim=optim, action_space=env.action_space, estimation_step=args.n_step, discount_factor=args.gamma, target_update_freq=args.target_update_freq, ) if args.resume_path: agent_learn.load_state_dict(torch.load(args.resume_path)) if agent_opponent is None: if args.opponent_path: agent_opponent = deepcopy(agent_learn) agent_opponent.load_state_dict(torch.load(args.opponent_path)) else: agent_opponent = RandomPolicy(action_space=env.action_space) if args.agent_id == 1: agents = [agent_learn, agent_opponent] else: agents = [agent_opponent, agent_learn] policy = MultiAgentPolicyManager(policies=agents, env=env) return policy, optim, env.agents def train_agent( args: argparse.Namespace = get_args(), agent_learn: BasePolicy | None = None, agent_opponent: BasePolicy | None = None, optim: torch.optim.Optimizer | None = 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, agent_learn=agent_learn, agent_opponent=agent_opponent, optim=optim, ) # 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) # policy.set_eps(1) train_collector.collect(n_step=args.batch_size * args.training_num) # log log_path = os.path.join(args.logdir, "tic_tac_toe", "dqn") writer = SummaryWriter(log_path) writer.add_text("args", str(args)) logger = TensorboardLogger(writer) def save_best_fn(policy: BasePolicy) -> None: if hasattr(args, "model_save_path"): model_save_path = args.model_save_path else: model_save_path = os.path.join(args.logdir, "tic_tac_toe", "dqn", "policy.pth") torch.save(policy.policies[agents[args.agent_id - 1]].state_dict(), model_save_path) def stop_fn(mean_rewards: float) -> bool: return mean_rewards >= args.win_rate def train_fn(epoch: int, env_step: int) -> None: policy.policies[agents[args.agent_id - 1]].set_eps(args.eps_train) def test_fn(epoch: int, env_step: int | None) -> None: policy.policies[agents[args.agent_id - 1]].set_eps(args.eps_test) def reward_metric(rews): return rews[:, args.agent_id - 1] # trainer result = OffpolicyTrainer( policy=policy, train_collector=train_collector, test_collector=test_collector, max_epoch=args.epoch, step_per_epoch=args.step_per_epoch, step_per_collect=args.step_per_collect, episode_per_test=args.test_num, batch_size=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, ).run() return result, policy.policies[agents[args.agent_id - 1]] def watch( args: argparse.Namespace = get_args(), agent_learn: BasePolicy | None = None, agent_opponent: BasePolicy | None = None, ) -> None: env = DummyVectorEnv([partial(get_env, render_mode="human")]) policy, optim, agents = get_agents(args, agent_learn=agent_learn, agent_opponent=agent_opponent) policy.eval() policy.policies[agents[args.agent_id - 1]].set_eps(args.eps_test) 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[:, args.agent_id - 1].mean()}, length: {lens.mean()}")