import argparse import os import pprint from typing import cast import gymnasium as gym import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from tianshou.data import ( Collector, PrioritizedVectorReplayBuffer, ReplayBuffer, VectorReplayBuffer, ) from tianshou.env import DummyVectorEnv from tianshou.policy import IQNPolicy from tianshou.policy.base import BasePolicy from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import Net from tianshou.utils.net.discrete import ImplicitQuantileNetwork from tianshou.utils.space_info import SpaceInfo def get_args() -> argparse.Namespace: parser = argparse.ArgumentParser() parser.add_argument("--task", type=str, default="CartPole-v0") parser.add_argument("--reward-threshold", type=float, default=None) parser.add_argument("--seed", type=int, default=0) 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=3e-3) parser.add_argument("--gamma", type=float, default=0.9) parser.add_argument("--sample-size", type=int, default=32) parser.add_argument("--online-sample-size", type=int, default=8) parser.add_argument("--target-sample-size", type=int, default=8) parser.add_argument("--num-cosines", type=int, default=64) 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=10) parser.add_argument("--step-per-epoch", type=int, default=10000) 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=[64, 64, 64]) parser.add_argument("--training-num", type=int, default=10) 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("--prioritized-replay", action="store_true", default=False) parser.add_argument("--alpha", type=float, default=0.6) parser.add_argument("--beta", type=float, default=0.4) parser.add_argument( "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", ) return parser.parse_known_args()[0] def test_iqn(args: argparse.Namespace = get_args()) -> None: env = gym.make(args.task) space_info = SpaceInfo.from_env(env) env.action_space = cast(gym.spaces.Discrete, env.action_space) args.state_shape = space_info.observation_info.obs_shape args.action_shape = space_info.action_info.action_shape if args.reward_threshold is None: default_reward_threshold = {"CartPole-v0": 195} args.reward_threshold = default_reward_threshold.get( args.task, env.spec.reward_threshold if env.spec else None, ) # train_envs = gym.make(args.task) # you can also use tianshou.env.SubprocVectorEnv 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 feature_net = Net( args.state_shape, args.hidden_sizes[-1], hidden_sizes=args.hidden_sizes[:-1], device=args.device, softmax=False, ) net = ImplicitQuantileNetwork( feature_net, args.action_shape, num_cosines=args.num_cosines, device=args.device, ) optim = torch.optim.Adam(net.parameters(), lr=args.lr) policy: IQNPolicy = IQNPolicy( model=net, optim=optim, action_space=env.action_space, discount_factor=args.gamma, sample_size=args.sample_size, online_sample_size=args.online_sample_size, target_sample_size=args.target_sample_size, estimation_step=args.n_step, target_update_freq=args.target_update_freq, ).to(args.device) # buffer buf: ReplayBuffer if args.prioritized_replay: buf = PrioritizedVectorReplayBuffer( args.buffer_size, buffer_num=len(train_envs), alpha=args.alpha, beta=args.beta, ) else: buf = VectorReplayBuffer(args.buffer_size, buffer_num=len(train_envs)) # collector train_collector = Collector(policy, train_envs, buf, 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, args.task, "iqn") writer = SummaryWriter(log_path) logger = TensorboardLogger(writer) 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 train_fn(epoch: int, env_step: int) -> None: # eps annnealing, just a demo if env_step <= 10000: policy.set_eps(args.eps_train) elif env_step <= 50000: eps = args.eps_train - (env_step - 10000) / 40000 * (0.9 * args.eps_train) policy.set_eps(eps) else: policy.set_eps(0.1 * args.eps_train) def test_fn(epoch: int, env_step: int | None) -> None: policy.set_eps(args.eps_test) # 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, logger=logger, update_per_step=args.update_per_step, ).run() assert stop_fn(result.best_reward) if __name__ == "__main__": pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() policy.set_eps(args.eps_test) collector = Collector(policy, env) collector_stats = collector.collect(n_episode=1, render=args.render) print(collector_stats) def test_piqn(args: argparse.Namespace = get_args()) -> None: args.prioritized_replay = True args.gamma = 0.95 test_iqn(args) if __name__ == "__main__": test_iqn(get_args())