import argparse import os import pickle 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 RainbowPolicy from tianshou.policy.base import BasePolicy from tianshou.policy.modelfree.rainbow import RainbowTrainingStats from tianshou.trainer import OffpolicyTrainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import Net from tianshou.utils.net.discrete import NoisyLinear 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("--num-atoms", type=int, default=51) parser.add_argument("--v-min", type=float, default=-10.0) parser.add_argument("--v-max", type=float, default=10.0) parser.add_argument("--noisy-std", type=float, default=0.1) 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=8000) parser.add_argument("--step-per-collect", type=int, default=8) parser.add_argument("--update-per-step", type=float, default=0.125) 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=8) 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("--beta-final", type=float, default=1.0) parser.add_argument("--resume", action="store_true") parser.add_argument( "--device", type=str, default="cuda" if torch.cuda.is_available() else "cpu", ) parser.add_argument("--save-interval", type=int, default=4) return parser.parse_known_args()[0] def test_rainbow(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) # model def noisy_linear(x: int, y: int) -> NoisyLinear: return NoisyLinear(x, y, args.noisy_std) net = Net( state_shape=args.state_shape, action_shape=args.action_shape, hidden_sizes=args.hidden_sizes, device=args.device, softmax=True, num_atoms=args.num_atoms, dueling_param=({"linear_layer": noisy_linear}, {"linear_layer": noisy_linear}), ) optim = torch.optim.Adam(net.parameters(), lr=args.lr) policy: RainbowPolicy[RainbowTrainingStats] = RainbowPolicy( model=net, optim=optim, discount_factor=args.gamma, action_space=env.action_space, num_atoms=args.num_atoms, v_min=args.v_min, v_max=args.v_max, estimation_step=args.n_step, target_update_freq=args.target_update_freq, ).to(args.device) # buffer buf: PrioritizedVectorReplayBuffer | VectorReplayBuffer if args.prioritized_replay: buf = PrioritizedVectorReplayBuffer( args.buffer_size, buffer_num=len(train_envs), alpha=args.alpha, beta=args.beta, weight_norm=True, ) 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, "rainbow") writer = SummaryWriter(log_path) logger = TensorboardLogger(writer, save_interval=args.save_interval) 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 annealing, 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) # beta annealing, just a demo if args.prioritized_replay: if env_step <= 10000: beta = args.beta elif env_step <= 50000: beta = args.beta - (env_step - 10000) / 40000 * (args.beta - args.beta_final) else: beta = args.beta_final buf.set_beta(beta) def test_fn(epoch: int, env_step: int | None) -> None: policy.set_eps(args.eps_test) def save_checkpoint_fn(epoch: int, env_step: int, gradient_step: int) -> str: # see also: https://pytorch.org/tutorials/beginner/saving_loading_models.html ckpt_path = os.path.join(log_path, "checkpoint.pth") # Example: saving by epoch num # ckpt_path = os.path.join(log_path, f"checkpoint_{epoch}.pth") torch.save( { "model": policy.state_dict(), "optim": optim.state_dict(), }, ckpt_path, ) buffer_path = os.path.join(log_path, "train_buffer.pkl") with open(buffer_path, "wb") as f: pickle.dump(train_collector.buffer, f) return ckpt_path if args.resume: # load from existing checkpoint print(f"Loading agent under {log_path}") ckpt_path = os.path.join(log_path, "checkpoint.pth") if os.path.exists(ckpt_path): checkpoint = torch.load(ckpt_path, map_location=args.device) policy.load_state_dict(checkpoint["model"]) policy.optim.load_state_dict(checkpoint["optim"]) print("Successfully restore policy and optim.") else: print("Fail to restore policy and optim.") buffer_path = os.path.join(log_path, "train_buffer.pkl") if os.path.exists(buffer_path): with open(buffer_path, "rb") as f: train_collector.buffer = pickle.load(f) print("Successfully restore buffer.") else: print("Fail to restore buffer.") # 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, resume_from_log=args.resume, save_checkpoint_fn=save_checkpoint_fn, ).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_rainbow_resume(args: argparse.Namespace = get_args()) -> None: args.resume = True test_rainbow(args) def test_prainbow(args: argparse.Namespace = get_args()) -> None: args.prioritized_replay = True args.gamma = 0.95 args.seed = 1 test_rainbow(args) if __name__ == "__main__": test_rainbow(get_args())