195 lines
6.9 KiB
Python
195 lines
6.9 KiB
Python
import argparse
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import os
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import warnings
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import gymnasium as gym
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import numpy as np
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import torch
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from pettingzoo.butterfly import pistonball_v6
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, InfoStats, VectorReplayBuffer
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from tianshou.env import DummyVectorEnv
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from tianshou.env.pettingzoo_env import PettingZooEnv
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from tianshou.policy import BasePolicy, DQNPolicy, MultiAgentPolicyManager
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from tianshou.trainer import OffpolicyTrainer
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from tianshou.utils import TensorboardLogger
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from tianshou.utils.net.common import Net
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def get_parser() -> argparse.ArgumentParser:
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parser = argparse.ArgumentParser()
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parser.add_argument("--seed", type=int, default=1626)
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parser.add_argument("--eps-test", type=float, default=0.05)
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parser.add_argument("--eps-train", type=float, default=0.1)
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parser.add_argument("--buffer-size", type=int, default=2000)
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parser.add_argument("--lr", type=float, default=1e-3)
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parser.add_argument(
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"--gamma",
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type=float,
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default=0.9,
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help="a smaller gamma favors earlier win",
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)
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parser.add_argument(
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"--n-pistons",
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type=int,
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default=3,
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help="Number of pistons(agents) in the env",
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)
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parser.add_argument("--n-step", type=int, default=100)
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parser.add_argument("--target-update-freq", type=int, default=320)
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parser.add_argument("--epoch", type=int, default=3)
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parser.add_argument("--step-per-epoch", type=int, default=500)
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parser.add_argument("--step-per-collect", type=int, default=10)
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parser.add_argument("--update-per-step", type=float, default=0.1)
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parser.add_argument("--batch-size", type=int, default=100)
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parser.add_argument("--hidden-sizes", type=int, nargs="*", default=[64, 64])
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parser.add_argument("--training-num", type=int, default=10)
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parser.add_argument("--test-num", type=int, default=10)
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parser.add_argument("--logdir", type=str, default="log")
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parser.add_argument("--render", type=float, default=0.0)
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parser.add_argument(
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"--watch",
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default=False,
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action="store_true",
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help="no training, watch the play of pre-trained models",
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)
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parser.add_argument(
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"--device",
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type=str,
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default="cuda" if torch.cuda.is_available() else "cpu",
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)
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return parser
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def get_args() -> argparse.Namespace:
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parser = get_parser()
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return parser.parse_known_args()[0]
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def get_env(args: argparse.Namespace = get_args()) -> PettingZooEnv:
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return PettingZooEnv(pistonball_v6.env(continuous=False, n_pistons=args.n_pistons))
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def get_agents(
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args: argparse.Namespace = get_args(),
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agents: list[BasePolicy] | None = None,
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optims: list[torch.optim.Optimizer] | None = None,
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) -> tuple[BasePolicy, list[torch.optim.Optimizer] | None, list]:
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env = get_env()
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observation_space = (
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env.observation_space["observation"]
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if isinstance(env.observation_space, gym.spaces.Dict)
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else env.observation_space
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)
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args.state_shape = observation_space.shape or int(observation_space.n)
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args.action_shape = env.action_space.shape or int(env.action_space.n)
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if agents is None:
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agents = []
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optims = []
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for _ in range(args.n_pistons):
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# model
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net = Net(
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state_shape=args.state_shape,
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action_shape=args.action_shape,
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hidden_sizes=args.hidden_sizes,
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device=args.device,
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).to(args.device)
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optim = torch.optim.Adam(net.parameters(), lr=args.lr)
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agent: DQNPolicy = DQNPolicy(
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model=net,
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optim=optim,
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action_space=env.action_space,
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discount_factor=args.gamma,
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estimation_step=args.n_step,
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target_update_freq=args.target_update_freq,
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)
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agents.append(agent)
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optims.append(optim)
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policy = MultiAgentPolicyManager(policies=agents, env=env)
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return policy, optims, env.agents
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def train_agent(
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args: argparse.Namespace = get_args(),
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agents: list[BasePolicy] | None = None,
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optims: list[torch.optim.Optimizer] | None = None,
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) -> tuple[InfoStats, BasePolicy]:
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train_envs = DummyVectorEnv([get_env for _ in range(args.training_num)])
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test_envs = DummyVectorEnv([get_env for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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policy, optim, agents = get_agents(args, agents=agents, optims=optims)
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# collector
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train_collector = Collector(
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policy,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True,
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)
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test_collector = Collector(policy, test_envs, exploration_noise=True)
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train_collector.reset()
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train_collector.collect(n_step=args.batch_size * args.training_num)
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# log
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log_path = os.path.join(args.logdir, "pistonball", "dqn")
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writer = SummaryWriter(log_path)
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writer.add_text("args", str(args))
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logger = TensorboardLogger(writer)
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def save_best_fn(policy: BasePolicy) -> None:
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pass
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def stop_fn(mean_rewards: float) -> bool:
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return False
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def train_fn(epoch: int, env_step: int) -> None:
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[agent.set_eps(args.eps_train) for agent in policy.policies.values()]
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def test_fn(epoch: int, env_step: int | None) -> None:
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[agent.set_eps(args.eps_test) for agent in policy.policies.values()]
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def reward_metric(rews: np.ndarray) -> np.ndarray:
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return rews[:, 0]
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# trainer
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result = OffpolicyTrainer(
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policy=policy,
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train_collector=train_collector,
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test_collector=test_collector,
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max_epoch=args.epoch,
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step_per_epoch=args.step_per_epoch,
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step_per_collect=args.step_per_collect,
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episode_per_test=args.test_num,
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batch_size=args.batch_size,
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train_fn=train_fn,
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test_fn=test_fn,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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update_per_step=args.update_per_step,
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logger=logger,
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test_in_train=False,
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reward_metric=reward_metric,
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).run()
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return result, policy
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def watch(args: argparse.Namespace = get_args(), policy: BasePolicy | None = None) -> None:
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env = DummyVectorEnv([get_env])
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if not policy:
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warnings.warn(
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"watching random agents, as loading pre-trained policies is currently not supported",
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)
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policy, _, _ = get_agents(args)
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[agent.set_eps(args.eps_test) for agent in policy.policies.values()]
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collector = Collector(policy, env, exploration_noise=True)
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result = collector.collect(n_episode=1, render=args.render, is_eval=True)
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result.pprint_asdict()
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