5ed6c1c7aa
This PR separates the `global_step` into `env_step` and `gradient_step`. In the future, the data from the collecting state will be stored under `env_step`, and the data from the updating state will be stored under `gradient_step`. Others: - add `rew_std` and `best_result` into the monitor - fix network unbounded in `test/continuous/test_sac_with_il.py` and `examples/box2d/bipedal_hardcore_sac.py` - change the dependency of ray to 1.0.0 since ray-project/ray#10134 has been resolved
163 lines
6.3 KiB
Python
163 lines
6.3 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.env import SubprocVectorEnv
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from tianshou.trainer import offpolicy_trainer
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from tianshou.data import Collector, ReplayBuffer
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from tianshou.policy import SACPolicy
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from tianshou.utils.net.common import Net
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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="BipedalWalkerHardcore-v3")
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parser.add_argument('--seed', type=int, default=0)
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parser.add_argument('--buffer-size', type=int, default=1000000)
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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=1e-3)
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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('--alpha', type=float, default=0.1)
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parser.add_argument('--auto_alpha', type=int, default=1)
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parser.add_argument('--alpha_lr', type=float, default=3e-4)
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parser.add_argument('--epoch', type=int, default=100)
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parser.add_argument('--step-per-epoch', type=int, default=10000)
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parser.add_argument('--collect-per-step', type=int, default=10)
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parser.add_argument('--batch-size', type=int, default=128)
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parser.add_argument('--layer-num', type=int, default=1)
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parser.add_argument('--training-num', type=int, default=8)
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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=int, default=0)
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parser.add_argument('--ignore-done', type=int, default=0)
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parser.add_argument('--n-step', type=int, default=4)
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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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parser.add_argument('--resume_path', type=str, default=None)
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return parser.parse_args()
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class EnvWrapper(object):
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"""Env wrapper for reward scale, action repeat and action noise"""
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def __init__(self, task, action_repeat=3, reward_scale=5, act_noise=0.0):
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self._env = gym.make(task)
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self.action_repeat = action_repeat
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self.reward_scale = reward_scale
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self.act_noise = act_noise
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def __getattr__(self, name):
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return getattr(self._env, name)
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def step(self, action):
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# add action noise
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action += self.act_noise * (-2 * np.random.random(4) + 1)
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r = 0.0
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for _ in range(self.action_repeat):
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obs_, reward_, done_, info_ = self._env.step(action)
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# remove done reward penalty
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if done_:
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break
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r = r + reward_
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# scale reward
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return obs_, self.reward_scale * r, done_, info_
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def test_sac_bipedal(args=get_args()):
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env = EnvWrapper(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 = SubprocVectorEnv(
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[lambda: EnvWrapper(args.task) for _ in range(args.training_num)])
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv([lambda: EnvWrapper(args.task, reward_scale=1)
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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_a = Net(args.layer_num, args.state_shape, device=args.device)
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actor = ActorProb(
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net_a, args.action_shape, args.max_action, 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.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic1 = Critic(net_c1, 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.layer_num, args.state_shape,
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args.action_shape, concat=True, device=args.device)
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critic2 = Critic(net_c2, 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,
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ignore_done=args.ignore_done,
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estimation_step=args.n_step)
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# load a previous policy
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if args.resume_path:
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policy.load_state_dict(torch.load(args.resume_path))
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print("Loaded agent from: ", args.resume_path)
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# collector
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train_collector = Collector(
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policy, train_envs, ReplayBuffer(args.buffer_size))
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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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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.collect_per_step, args.test_num,
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args.batch_size, stop_fn=stop_fn, save_fn=save_fn, writer=writer,
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test_in_train=False)
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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=[1] * args.test_num,
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render=args.render)
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print(f'Final reward: {result["rew"]}, length: {result["len"]}')
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if __name__ == '__main__':
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test_sac_bipedal()
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