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 OffpolicyTrainer 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 = 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 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()}")