9b61bc620c
This PR focus on refactor of logging method to solve bug of nan reward and log interval. After these two pr, hopefully fundamental change of tianshou/data is finished. We then can concentrate on building benchmarks of tianshou finally. Things changed: 1. trainer now accepts logger (BasicLogger or LazyLogger) instead of writer; 2. remove utils.SummaryWriter;
136 lines
5.6 KiB
Python
136 lines
5.6 KiB
Python
import os
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import gym
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import torch
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import pprint
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import argparse
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import numpy as np
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.policy import SACPolicy
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from tianshou.utils import BasicLogger
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from tianshou.env import DummyVectorEnv
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from tianshou.exploration import OUNoise
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from tianshou.utils.net.common import Net
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.utils.net.continuous import ActorProb, Critic
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def get_args():
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parser = argparse.ArgumentParser()
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parser.add_argument('--task', type=str, default='MountainCarContinuous-v0')
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parser.add_argument('--seed', type=int, default=1626)
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parser.add_argument('--buffer-size', type=int, default=50000)
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parser.add_argument('--actor-lr', type=float, default=3e-4)
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parser.add_argument('--critic-lr', type=float, default=3e-4)
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parser.add_argument('--alpha-lr', type=float, default=3e-4)
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parser.add_argument('--noise_std', type=float, default=1.2)
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parser.add_argument('--gamma', type=float, default=0.99)
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parser.add_argument('--tau', type=float, default=0.005)
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parser.add_argument('--auto_alpha', type=int, default=1)
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parser.add_argument('--alpha', type=float, default=0.2)
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parser.add_argument('--epoch', type=int, default=20)
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parser.add_argument('--step-per-epoch', type=int, default=12000)
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parser.add_argument('--step-per-collect', type=int, default=5)
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parser.add_argument('--update-per-step', type=float, default=0.2)
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parser.add_argument('--batch-size', type=int, default=128)
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parser.add_argument('--hidden-sizes', type=int,
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nargs='*', default=[128, 128])
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parser.add_argument('--training-num', type=int, default=5)
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parser.add_argument('--test-num', type=int, default=100)
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parser.add_argument('--logdir', type=str, default='log')
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parser.add_argument('--render', type=float, default=0.)
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parser.add_argument('--rew-norm', type=bool, default=False)
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parser.add_argument(
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'--device', type=str,
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default='cuda' if torch.cuda.is_available() else 'cpu')
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return parser.parse_args()
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def test_sac(args=get_args()):
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env = gym.make(args.task)
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args.state_shape = env.observation_space.shape or env.observation_space.n
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args.action_shape = env.action_space.shape or env.action_space.n
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args.max_action = env.action_space.high[0]
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# train_envs = gym.make(args.task)
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train_envs = DummyVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = DummyVectorEnv(
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[lambda: gym.make(args.task) for _ in range(args.test_num)])
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# seed
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np.random.seed(args.seed)
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torch.manual_seed(args.seed)
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train_envs.seed(args.seed)
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test_envs.seed(args.seed)
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# model
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net = Net(args.state_shape, hidden_sizes=args.hidden_sizes,
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device=args.device)
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actor = ActorProb(
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net, args.action_shape,
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max_action=args.max_action, device=args.device, unbounded=True
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).to(args.device)
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actor_optim = torch.optim.Adam(actor.parameters(), lr=args.actor_lr)
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net_c1 = Net(args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes, concat=True,
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device=args.device)
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critic1 = Critic(net_c1, device=args.device).to(args.device)
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critic1_optim = torch.optim.Adam(critic1.parameters(), lr=args.critic_lr)
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net_c2 = Net(args.state_shape, args.action_shape,
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hidden_sizes=args.hidden_sizes, concat=True,
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device=args.device)
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critic2 = Critic(net_c2, device=args.device).to(args.device)
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critic2_optim = torch.optim.Adam(critic2.parameters(), lr=args.critic_lr)
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if args.auto_alpha:
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target_entropy = -np.prod(env.action_space.shape)
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log_alpha = torch.zeros(1, requires_grad=True, device=args.device)
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alpha_optim = torch.optim.Adam([log_alpha], lr=args.alpha_lr)
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args.alpha = (target_entropy, log_alpha, alpha_optim)
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policy = SACPolicy(
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actor, actor_optim, critic1, critic1_optim, critic2, critic2_optim,
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action_range=[env.action_space.low[0], env.action_space.high[0]],
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tau=args.tau, gamma=args.gamma, alpha=args.alpha,
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reward_normalization=args.rew_norm, ignore_done=True,
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exploration_noise=OUNoise(0.0, args.noise_std))
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# collector
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train_collector = Collector(
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policy, train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True)
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test_collector = Collector(policy, test_envs)
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# train_collector.collect(n_step=args.buffer_size)
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# log
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log_path = os.path.join(args.logdir, args.task, 'sac')
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writer = SummaryWriter(log_path)
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logger = BasicLogger(writer)
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def save_fn(policy):
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torch.save(policy.state_dict(), os.path.join(log_path, 'policy.pth'))
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def stop_fn(mean_rewards):
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return mean_rewards >= env.spec.reward_threshold
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# trainer
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result = offpolicy_trainer(
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policy, train_collector, test_collector, args.epoch,
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args.step_per_epoch, args.step_per_collect, args.test_num, args.batch_size,
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update_per_step=args.update_per_step, stop_fn=stop_fn,
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save_fn=save_fn, logger=logger)
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assert stop_fn(result['best_reward'])
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if __name__ == '__main__':
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pprint.pprint(result)
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# Let's watch its performance!
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policy.eval()
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test_envs.seed(args.seed)
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test_collector.reset()
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result = test_collector.collect(n_episode=args.test_num, render=args.render)
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rews, lens = result["rews"], result["lens"]
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print(f"Final reward: {rews.mean()}, length: {lens.mean()}")
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if __name__ == '__main__':
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test_sac()
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