import os import gym import torch import pprint import argparse import numpy as np from torch.utils.tensorboard import SummaryWriter from tianshou.utils import TensorboardLogger from tianshou.env import DummyVectorEnv from tianshou.utils.net.common import Net from tianshou.trainer import offpolicy_trainer from tianshou.data import Collector, VectorReplayBuffer from tianshou.policy import SACPolicy, ImitationPolicy from tianshou.utils.net.continuous import Actor, ActorProb, Critic def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default='Pendulum-v0') parser.add_argument('--seed', type=int, default=0) parser.add_argument('--buffer-size', type=int, default=20000) parser.add_argument('--actor-lr', type=float, default=1e-3) parser.add_argument('--critic-lr', type=float, default=1e-3) parser.add_argument('--il-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', type=int, default=1) parser.add_argument('--alpha-lr', type=float, default=3e-4) parser.add_argument('--epoch', type=int, default=5) parser.add_argument('--step-per-epoch', type=int, default=24000) parser.add_argument('--il-step-per-epoch', type=int, default=500) 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=128) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128]) parser.add_argument('--imitation-hidden-sizes', type=int, nargs='*', default=[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.) parser.add_argument('--rew-norm', action="store_true", default=False) parser.add_argument('--n-step', type=int, default=3) 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_sac_with_il(args=get_args()): torch.set_num_threads(1) # we just need only one thread for NN env = gym.make(args.task) if args.task == 'Pendulum-v0': env.spec.reward_threshold = -250 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] # 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).to(args.device) actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr) net_c1 = Net(args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device) critic1 = Critic(net_c1, device=args.device).to(args.device) critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr) net_c2 = Net(args.state_shape, args.action_shape, hidden_sizes=args.hidden_sizes, concat=True, device=args.device) critic2 = Critic(net_c2, device=args.device).to(args.device) critic2_optim = torch.optim.Adam(critic2.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 = SACPolicy( actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim, tau=args.tau, gamma=args.gamma, alpha=args.alpha, reward_normalization=args.rew_norm, estimation_step=args.n_step, 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.buffer_size) # log log_path = os.path.join(args.logdir, args.task, 'sac') writer = SummaryWriter(log_path) logger = TensorboardLogger(writer) def save_fn(policy): torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth')) def stop_fn(mean_rewards): return mean_rewards >= env.spec.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_fn=save_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()}") # here we define an imitation collector with a trivial policy policy.eval() if args.task == 'Pendulum-v0': env.spec.reward_threshold = -300 # lower the goal net = Actor( Net(args.state_shape, hidden_sizes=args.imitation_hidden_sizes, device=args.device), args.action_shape, max_action=args.max_action, device=args.device ).to(args.device) optim = torch.optim.Adam(net.parameters(), lr=args.il_lr) il_policy = ImitationPolicy( net, optim, action_space=env.action_space, action_scaling=True, action_bound_method="clip") il_test_collector = Collector( il_policy, DummyVectorEnv([lambda: gym.make(args.task) for _ in range(args.test_num)]) ) train_collector.reset() result = offpolicy_trainer( il_policy, train_collector, il_test_collector, args.epoch, args.il_step_per_epoch, args.step_per_collect, args.test_num, args.batch_size, stop_fn=stop_fn, save_fn=save_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) il_policy.eval() collector = Collector(il_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_sac_with_il()