PPO 跑CartPole-v1-Toy模板网

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gym-0.26.2
cartPole-v1

参考动手学强化学习书中的代码,并做了一些修改

代码

import gym
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
import matplotlib.pyplot as plt
from tqdm import tqdm


class PolicyNet(nn.Module):
    def __init__(self, state_dim, hidden_dim, action_dim):
        super().__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim)
        self.fc2 = 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(nn.Module):
    def __init__(self, state_dim, hidden_dim):
        super().__init__()
        self.fc1 = nn.Linear(state_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, 1)

    def forward(self, x):
        x = F.relu(self.fc1(x))
        return self.fc2(x)


class PPO:
    """PPO算法，采用截断的方式"""
    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 中阶段范围的参数
        self.device = device

    def take_action(self, state):
        state = torch.FloatTensor([state]).to(self.device)
        probs = self.actor(state)
        action_dist = torch.distributions.Categorical(probs)
        action = action_dist.sample()
        return action.item()

    def gae(self, td_delta):
        td_delta = td_delta.detach().numpy()
        advantages_list = []
        advantage = 0.0
        for delta in td_delta[::-1]:
            advantage = self.gamma * self.lmbda * advantage + delta
            advantages_list.append(advantage)
        advantages_list.reverse()
        return torch.FloatTensor(advantages_list)

    def update(self, transition_dist):
        states = torch.FloatTensor(transition_dist['states']).to(self.device)
        actions = torch.tensor(transition_dist['actions']).reshape((-1, 1)).to(self.device)
        rewards = torch.FloatTensor(transition_dist['rewards']).reshape((-1, 1)).to(self.device)
        next_states = torch.FloatTensor(transition_dist['next_states']).to(self.device)
        dones = torch.FloatTensor(transition_dist['dones']).reshape((-1, 1)).to(self.device)
        td_target = rewards + self.gamma * self.critic(next_states) * (1 - dones)
        td_delta = td_target - self.critic(states)
        # GAE 计算广义优势
        advantage = self.gae(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))
            ration = torch.exp(log_probs - old_log_probs)
            surr1 = ration * advantage
            surr2 = torch.clamp(ration, 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()


def moving_average(a, window_size):
    cumulative_sum = np.cumsum(np.insert(a, 0, 0))
    middle = (cumulative_sum[window_size:] - cumulative_sum[:-window_size]) / window_size
    r = np.arange(1, window_size-1, 2)
    begin = np.cumsum(a[:window_size-1])[::2] / r
    end = (np.cumsum(a[:-window_size:-1])[::2] / r)[::-1]
    return np.concatenate((begin, middle, end))


def train():
    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:0" if torch.cuda.is_available() else "cpu")

    env_name = "CartPole-v1"
    env = gym.make(env_name)
    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 = []
    for i in range(10):
        with tqdm(total=int(num_episodes / 10), desc='Iteration %d' % i) as pbar:
            for i_episode in range(int(num_episodes / 10)):
                episode_return = 0
                transition_dict = {'states': [], 'actions': [], 'next_states': [], 'rewards': [], 'dones': []}
                state, _ = env.reset()
                done, truncated = False, False
                while not done and not truncated:
                    action = agent.take_action(state)
                    next_state, reward, done, truncated, _ = env.step(action)
                    done = done or truncated    # 这个地方要注意
                    transition_dict['states'].append(state)
                    transition_dict['actions'].append(action)
                    transition_dict['next_states'].append(next_state)
                    transition_dict['rewards'].append(reward)
                    transition_dict['dones'].append(done)
                    state = next_state
                    episode_return += reward
                return_list.append(episode_return)
                agent.update(transition_dict)
                if (i_episode + 1) % 10 == 0:
                    pbar.set_postfix({'episode': '%d' % (num_episodes / 10 * i + i_episode + 1),
                                      'return': '%.3f' % np.mean(return_list[-10:])})
                pbar.update(1)


    episodes_list = list(range(len(return_list)))
    plt.plot(episodes_list, return_list)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title(f'PPO on {env_name}')
    plt.show()

    mv_return = moving_average(return_list, 9)
    plt.plot(episodes_list, mv_return)
    plt.xlabel('Episodes')
    plt.ylabel('Returns')
    plt.title(f'PPO on {env_name}')
    plt.show()


if __name__ == '__main__':
    train()