import argparse import os import pprint import gymnasium as gym import numpy as np import torch from torch.utils.tensorboard import SummaryWriter from tianshou.data import Collector, PrioritizedVectorReplayBuffer, VectorReplayBuffer from tianshou.env import DummyVectorEnv from tianshou.policy import DQNPolicy, ICMPolicy from tianshou.policy.base import BasePolicy from tianshou.policy.modelfree.dqn import DQNTrainingStats from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import MLP, Net from tianshou.utils.net.discrete import IntrinsicCuriosityModule 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=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-3) parser.add_argument("--gamma", type=float, default=0.9) 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=20) 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=[128, 128, 128, 128]) 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", ) parser.add_argument( "--lr-scale", type=float, default=1.0, help="use intrinsic curiosity module with this lr scale", ) parser.add_argument( "--reward-scale", type=float, default=0.01, help="scaling factor for intrinsic curiosity reward", ) parser.add_argument( "--forward-loss-weight", type=float, default=0.2, help="weight for the forward model loss in ICM", ) return parser.parse_known_args()[0] def test_dqn_icm(args: argparse.Namespace = get_args()) -> None: env = gym.make(args.task) assert isinstance(env.action_space, gym.spaces.Discrete) space_info = SpaceInfo.from_env(env) 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) # Q_param = V_param = {"hidden_sizes": [128]} # model net = Net( state_shape=args.state_shape, action_shape=args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device, # dueling=(Q_param, V_param), ).to(args.device) optim = torch.optim.Adam(net.parameters(), lr=args.lr) policy: DQNPolicy[DQNTrainingStats] = DQNPolicy( model=net, optim=optim, action_space=env.action_space, discount_factor=args.gamma, estimation_step=args.n_step, target_update_freq=args.target_update_freq, ) feature_dim = args.hidden_sizes[-1] obs_dim = space_info.observation_info.obs_dim feature_net = MLP( obs_dim, output_dim=feature_dim, hidden_sizes=args.hidden_sizes[:-1], device=args.device, ) action_dim = space_info.action_info.action_dim icm_net = IntrinsicCuriosityModule( feature_net, feature_dim, action_dim, hidden_sizes=args.hidden_sizes[-1:], device=args.device, ).to(args.device) icm_optim = torch.optim.Adam(icm_net.parameters(), lr=args.lr) policy: ICMPolicy = ICMPolicy( policy=policy, model=icm_net, optim=icm_optim, action_space=env.action_space, lr_scale=args.lr_scale, reward_scale=args.reward_scale, forward_loss_weight=args.forward_loss_weight, ) # buffer buf: PrioritizedVectorReplayBuffer | VectorReplayBuffer 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.reset() train_collector.collect(n_step=args.batch_size * args.training_num) # log log_path = os.path.join(args.logdir, args.task, "dqn_icm") 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, update_per_step=args.update_per_step, train_fn=train_fn, test_fn=test_fn, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger, ).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) if __name__ == "__main__": test_dqn_icm(get_args())