1423eeb3b2
- Fix the current bug discussed in #844 in `test_ppo.py`. - Add warning for `ActorProb ` if both `max_action ` and `unbounded=True` are used for model initializations. - Add warning for PGpolicy and DDPGpolicy if they find duplicate usage of action-bounded actor and action scaling method.
195 lines
6.6 KiB
Python
195 lines
6.6 KiB
Python
import argparse
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import os
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import pprint
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import gymnasium as gym
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import numpy as np
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from tianshou.data import Collector, VectorReplayBuffer
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from tianshou.env import SubprocVectorEnv
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from tianshou.policy import SACPolicy
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from tianshou.trainer import offpolicy_trainer
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from tianshou.utils import TensorboardLogger
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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=100000)
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parser.add_argument('--step-per-collect', type=int, default=10)
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parser.add_argument('--update-per-step', type=float, default=0.1)
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parser.add_argument('--batch-size', type=int, default=128)
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parser.add_argument('--hidden-sizes', type=int, nargs='*', default=[128, 128])
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parser.add_argument('--training-num', type=int, default=10)
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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('--n-step', type=int, default=4)
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parser.add_argument(
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'--device', type=str, default='cuda' if torch.cuda.is_available() else 'cpu'
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)
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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 Wrapper(gym.Wrapper):
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"""Env wrapper for reward scale, action repeat and removing done penalty"""
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def __init__(self, env, action_repeat=3, reward_scale=5, rm_done=True):
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super().__init__(env)
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self.action_repeat = action_repeat
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self.reward_scale = reward_scale
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self.rm_done = rm_done
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def step(self, action):
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rew_sum = 0.0
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for _ in range(self.action_repeat):
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obs, rew, done, info = self.env.step(action)
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# remove done reward penalty
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if not done or not self.rm_done:
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rew_sum = rew_sum + rew
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if done:
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break
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# scale reward
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return obs, self.reward_scale * rew_sum, done, info
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def test_sac_bipedal(args=get_args()):
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env = Wrapper(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 = SubprocVectorEnv(
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[lambda: Wrapper(gym.make(args.task)) for _ in range(args.training_num)]
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)
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# test_envs = gym.make(args.task)
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test_envs = SubprocVectorEnv(
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[
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lambda: Wrapper(gym.make(args.task), reward_scale=1, rm_done=False)
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for _ in range(args.test_num)
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]
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)
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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.state_shape, hidden_sizes=args.hidden_sizes, device=args.device)
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actor = ActorProb(net_a, args.action_shape, device=args.device,
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unbounded=True).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(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device
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)
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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(
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args.state_shape,
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args.action_shape,
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hidden_sizes=args.hidden_sizes,
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concat=True,
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device=args.device
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)
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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,
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actor_optim,
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critic1,
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critic1_optim,
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critic2,
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critic2_optim,
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tau=args.tau,
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gamma=args.gamma,
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alpha=args.alpha,
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estimation_step=args.n_step,
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action_space=env.action_space
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)
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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,
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train_envs,
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VectorReplayBuffer(args.buffer_size, len(train_envs)),
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exploration_noise=True
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)
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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 = TensorboardLogger(writer)
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def save_best_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,
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train_collector,
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test_collector,
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args.epoch,
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args.step_per_epoch,
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args.step_per_collect,
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args.test_num,
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args.batch_size,
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update_per_step=args.update_per_step,
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test_in_train=False,
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stop_fn=stop_fn,
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save_best_fn=save_best_fn,
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logger=logger
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)
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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_bipedal()
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