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, VectorReplayBuffer from tianshou.env import DummyVectorEnv from tianshou.policy import REDQPolicy from tianshou.trainer import offpolicy_trainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import EnsembleLinear, Net from tianshou.utils.net.continuous import ActorProb, Critic def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default='Pendulum-v1') parser.add_argument('--reward-threshold', type=float, default=None) parser.add_argument('--seed', type=int, default=0) parser.add_argument('--buffer-size', type=int, default=20000) parser.add_argument('--ensemble-size', type=int, default=4) parser.add_argument('--subset-size', type=int, default=2) parser.add_argument('--actor-lr', type=float, default=1e-4) parser.add_argument('--critic-lr', type=float, default=1e-3) parser.add_argument('--gamma', type=float, default=0.99) parser.add_argument('--tau', type=float, default=0.005) parser.add_argument('--alpha', type=float, default=0.2) parser.add_argument('--auto-alpha', action='store_true', default=False) parser.add_argument('--alpha-lr', type=float, default=3e-4) parser.add_argument("--start-timesteps", type=int, default=1000) parser.add_argument('--epoch', type=int, default=5) parser.add_argument('--step-per-epoch', type=int, default=5000) parser.add_argument('--step-per-collect', type=int, default=1) parser.add_argument('--update-per-step', type=int, default=3) parser.add_argument('--n-step', type=int, default=1) parser.add_argument('--batch-size', type=int, default=64) parser.add_argument( '--target-mode', type=str, choices=('min', 'mean'), default='min' ) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64]) 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.) parser.add_argument( '--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu' ) args = parser.parse_known_args()[0] return args def test_redq(args=get_args()): env = gym.make(args.task) args.state_shape = env.observation_space.shape or env.observation_space.n args.action_shape = env.action_space.shape or env.action_space.n args.max_action = env.action_space.high[0] if args.reward_threshold is None: default_reward_threshold = {"Pendulum-v0": -250, "Pendulum-v1": -250} args.reward_threshold = default_reward_threshold.get( args.task, env.spec.reward_threshold ) # you can also use tianshou.env.SubprocVectorEnv # train_envs = gym.make(args.task) 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 net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = ActorProb( net, args.action_shape, max_action=args.max_action, device=args.device, unbounded=True, conditioned_sigma=True ).to(args.device) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) def linear(x, y): return EnsembleLinear(args.ensemble_size, x, y) net_c = Net( args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device, linear_layer=linear, ) critic = Critic( net_c, device=args.device, linear_layer=linear, flatten_input=False ).to(args.device) critic_optim = torch.optim.Adam(critic.parameters(), lr=args.critic_lr) if args.auto_alpha: target_entropy = -np.prod(env.action_space.shape) log_alpha = torch.zeros(1, requires_grad=True, device=args.device) alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr) args.alpha = (target_entropy, log_alpha, alpha_optim) policy = REDQPolicy( actor, actor_optim, critic, critic_optim, args.ensemble_size, args.subset_size, tau=args.tau, gamma=args.gamma, alpha=args.alpha, estimation_step=args.n_step, actor_delay=args.update_per_step, target_mode=args.target_mode, action_space=env.action_space, ) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)), exploration_noise=True ) test_collector = Collector(policy, test_envs) train_collector.collect(n_step=args.start_timesteps, random=True) # log log_path = os.path.join(args.logdir, args.task, 'redq') writer = SummaryWriter(log_path) logger = TensorboardLogger(writer) def save_best_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth')) def stop_fn(mean_rewards): return mean_rewards >= args.reward_threshold # trainer result = offpolicy_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.step_per_collect, args.test_num, args.batch_size, update_per_step=args.update_per_step, stop_fn=stop_fn, save_best_fn=save_best_fn, logger=logger ) assert stop_fn(result['best_reward']) if __name__ == '__main__': pprint.pprint(result) # Let's watch its performance! env = gym.make(args.task) policy.eval() collector = Collector(policy, env) result = collector.collect(n_episode=1, render=args.render) rews, lens = result["rews"], result["lens"] print(f"Final reward: {rews.mean()}, length: {lens.mean()}") if __name__ == '__main__': test_redq()