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基于 Hands-on-RL/第12章-PPO算法.ipynb at main · boyu-ai/Hands-on-RL · GitHub
理论 PPO 算法
修改了警告和报错
运行环境
Debian GNU/Linux 12 Python 3.9.19 torch 2.0.1 gym 0.26.2运行代码
PPO.py
#!/usr/bin/env python import gym import torch import torch.nn.functional as F import numpy as np import matplotlib.pyplot as plt import rl_utils class PolicyNet(torch.nn.Module): def __init__(self, state_dim, hidden_dim, action_dim): super(PolicyNet, self).__init__() self.fc1 = torch.nn.Linear(state_dim, hidden_dim) self.fc2 = torch.nn.Linear(hidden_dim, action_dim) def forward(self, x): x = F.relu(self.fc1(x)) return F.softmax(self.fc2(x), dim=1) class ValueNet(torch.nn.Module): def __init__(self, state_dim, hidden_dim): super(ValueNet, self).__init__() self.fc1 = torch.nn.Linear(state_dim, hidden_dim) self.fc2 = torch.nn.Linear(hidden_dim, 1) def forward(self, x): x = F.relu(self.fc1(x)) return self.fc2(x) class PPO: ''' PPO算法,采用截断方式 ''' def __init__(self, state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda, epochs, eps, gamma, device): self.actor = PolicyNet(state_dim, hidden_dim, action_dim).to(device) self.critic = ValueNet(state_dim, hidden_dim).to(device) self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=actor_lr) self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), lr=critic_lr) self.gamma = gamma self.lmbda = lmbda self.epochs = epochs # 一条序列的数据用来训练轮数 self.eps = eps # PPO中截断范围的参数 self.device = device def take_action(self, state): state = torch.tensor(np.array([state]), dtype=torch.float).to(self.device) probs = self.actor(state) action_dist = torch.distributions.Categorical(probs) action = action_dist.sample() return action.item() def update(self, transition_dict): states = torch.tensor(np.array(transition_dict['states']), dtype=torch.float).to(self.device) actions = torch.tensor(transition_dict['actions']).view(-1, 1).to( self.device) rewards = torch.tensor(transition_dict['rewards'], dtype=torch.float).view(-1, 1).to(self.device) next_states = torch.tensor(np.array(transition_dict['next_states']), dtype=torch.float).to(self.device) dones = torch.tensor(transition_dict['dones'], dtype=torch.float).view(-1, 1).to(self.device) td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones) td_delta = td_target - self.critic(states) advantage = rl_utils.compute_advantage(self.gamma, self.lmbda, td_delta.cpu()).to(self.device) old_log_probs = torch.log(self.actor(states).gather(1, actions)).detach() for _ in range(self.epochs): log_probs = torch.log(self.actor(states).gather(1, actions)) ratio = torch.exp(log_probs - old_log_probs) surr1 = ratio * advantage surr2 = torch.clamp(ratio, 1 - self.eps, 1 + self.eps) * advantage # 截断 actor_loss = torch.mean(-torch.min(surr1, surr2)) # PPO损失函数 critic_loss = torch.mean( F.mse_loss(self.critic(states), td_target.detach())) self.actor_optimizer.zero_grad() self.critic_optimizer.zero_grad() actor_loss.backward() critic_loss.backward() self.actor_optimizer.step() self.critic_optimizer.step() actor_lr = 1e-3 critic_lr = 1e-2 num_episodes = 500 hidden_dim = 128 gamma = 0.98 lmbda = 0.95 epochs = 10 eps = 0.2 device = torch.device("cuda") if torch.cuda.is_available() else torch.device( "cpu") env_name = 'CartPole-v1' env = gym.make(env_name) env.reset(seed=0) torch.manual_seed(0) state_dim = env.observation_space.shape[0] action_dim = env.action_space.n agent = PPO(state_dim, hidden_dim, action_dim, actor_lr, critic_lr, lmbda, epochs, eps, gamma, device) return_list = rl_utils.train_on_policy_agent(env, agent, num_episodes) episodes_list = list(range(len(return_list))) plt.plot(episodes_list, return_list) plt.xlabel('Episodes') plt.ylabel('Returns') plt.title('PPO on {}'.format(env_name)) plt.show() mv_return = rl_utils.moving_average(return_list, 9) plt.plot(episodes_list, mv_return) plt.xlabel('Episodes') plt.ylabel('Returns') plt.title('PPO on {}'.format(env_name)) plt.show() rl_utils.py 参考
