import argparse import os import pprint import 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 ICMPolicy, PPOPolicy from tianshou.trainer import onpolicy_trainer from tianshou.utils import TensorboardLogger from tianshou.utils.net.common import MLP, ActorCritic, Net from tianshou.utils.net.discrete import Actor, Critic, IntrinsicCuriosityModule def get_args(): parser = argparse.ArgumentParser() parser.add_argument('--task', type=str, default='CartPole-v0') parser.add_argument('--seed', type=int, default=1626) parser.add_argument('--buffer-size', type=int, default=20000) parser.add_argument('--lr', type=float, default=3e-4) parser.add_argument('--gamma', type=float, default=0.99) parser.add_argument('--epoch', type=int, default=10) parser.add_argument('--step-per-epoch', type=int, default=50000) parser.add_argument('--step-per-collect', type=int, default=2000) parser.add_argument('--repeat-per-collect', type=int, default=10) parser.add_argument('--batch-size', type=int, default=64) parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[64, 64]) parser.add_argument('--training-num', type=int, default=20) 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' ) # ppo special parser.add_argument('--vf-coef', type=float, default=0.5) parser.add_argument('--ent-coef', type=float, default=0.0) parser.add_argument('--eps-clip', type=float, default=0.2) parser.add_argument('--max-grad-norm', type=float, default=0.5) parser.add_argument('--gae-lambda', type=float, default=0.95) parser.add_argument('--rew-norm', type=int, default=0) parser.add_argument('--norm-adv', type=int, default=0) parser.add_argument('--recompute-adv', type=int, default=0) parser.add_argument('--dual-clip', type=float, default=None) parser.add_argument('--value-clip', type=int, default=0) parser.add_argument( '--lr-scale', type=float, default=1., 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' ) args = parser.parse_known_args()[0] return args def test_ppo(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 # 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 net = Net(args.state_shape, hidden_sizes=args.hidden_sizes, device=args.device) actor = Actor(net, args.action_shape, device=args.device).to(args.device) critic = Critic(net, device=args.device).to(args.device) actor_critic = ActorCritic(actor, critic) # orthogonal initialization for m in actor_critic.modules(): if isinstance(m, torch.nn.Linear): torch.nn.init.orthogonal_(m.weight) torch.nn.init.zeros_(m.bias) optim = torch.optim.Adam(actor_critic.parameters(), lr=args.lr) dist = torch.distributions.Categorical policy = PPOPolicy( actor, critic, optim, dist, discount_factor=args.gamma, max_grad_norm=args.max_grad_norm, eps_clip=args.eps_clip, vf_coef=args.vf_coef, ent_coef=args.ent_coef, gae_lambda=args.gae_lambda, reward_normalization=args.rew_norm, dual_clip=args.dual_clip, value_clip=args.value_clip, action_space=env.action_space, deterministic_eval=True, advantage_normalization=args.norm_adv, recompute_advantage=args.recompute_adv ) feature_dim = args.hidden_sizes[-1] feature_net = MLP( np.prod(args.state_shape), output_dim=feature_dim, hidden_sizes=args.hidden_sizes[:-1], device=args.device ) action_dim = np.prod(args.action_shape) 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( policy, icm_net, icm_optim, args.lr_scale, args.reward_scale, args.forward_loss_weight ) # collector train_collector = Collector( policy, train_envs, VectorReplayBuffer(args.buffer_size, len(train_envs)) ) test_collector = Collector(policy, test_envs) # log log_path = os.path.join(args.logdir, args.task, 'ppo_icm') 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 = onpolicy_trainer( policy, train_collector, test_collector, args.epoch, args.step_per_epoch, args.repeat_per_collect, args.test_num, args.batch_size, step_per_collect=args.step_per_collect, 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()}") if __name__ == '__main__': test_ppo()