import os import gym import torch import pprint import argparse import numpy as np from torch.utils.tensorboard import SummaryWriter from tianshou.env import DummyVectorEnv from tianshou.utils.net.common import Net from tianshou.trainer import offpolicy_trainer from tianshou.data import Collector, ReplayBuffer 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=3e-4) 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('--epoch', type=int, default=20) parser.add_argument('--step-per-epoch', type=int, default=2400) parser.add_argument('--collect-per-step', type=int, default=10) 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=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('--rew-norm', type=int, default=1) parser.add_argument('--ignore-done', type=int, default=1) parser.add_argument('--n-step', type=int, default=4) 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) policy = SACPolicy( actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim, action_range=[env.action_space.low[0], env.action_space.high[0]], tau=args.tau, gamma=args.gamma, alpha=args.alpha, reward_normalization=args.rew_norm, ignore_done=args.ignore_done, estimation_step=args.n_step) # collector train_collector = Collector( policy, train_envs, ReplayBuffer(args.buffer_size)) 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) 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.collect_per_step, args.test_num, args.batch_size, stop_fn=stop_fn, save_fn=save_fn, writer=writer) 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) print(f'Final reward: {result["rew"]}, length: {result["len"]}') # 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, mode='continuous') 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.step_per_epoch // 5, args.collect_per_step, args.test_num, args.batch_size, stop_fn=stop_fn, save_fn=save_fn, writer=writer) 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) print(f'Final reward: {result["rew"]}, length: {result["len"]}') if __name__ == '__main__': test_sac_with_il()