Actor-Critic

Actor-Critic是一种强化学习算法，它结合了策略梯度和值函数的优点。在Actor-Critic算法中，Actor使用策略函数生成动作并与环境交互，Critic使用价值函数评估Actor的表现并指导Actor下一步的动作。Actor和Critic都是神经网络，需要进行梯度更新，互相依赖。Actor-Critic算法可以用于解决连续动作空间的问题，例如机器人控制和游戏AI等领域。

以下是一个Actor-Critic算法的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import gym

# 定义Actor神经网络
class Actor(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(Actor, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, output_dim)
        self.softmax = nn.Softmax(dim=-1)

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

# 定义Critic神经网络
class Critic(nn.Module):
    def __init__(self, input_dim):
        super(Critic, self).__init__()
        self.fc1 = nn.Linear(input_dim, 128)
        self.fc2 = nn.Linear(128, 1)

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

# 定义Actor-Critic算法
class ActorCritic:
    def __init__(self, env):
        self.env = env
        self.obs_dim = env.observation_space.shape[0]
        self.action_dim = env.action_space.n
        self.actor = Actor(self.obs_dim, self.action_dim)
        self.critic = Critic(self.obs_dim)
        self.actor_optimizer = optim.Adam(self.actor.parameters(), lr=0.01)
        self.critic_optimizer = optim.Adam(self.critic.parameters(), lr=0.01)

    def train(self, max_episodes=1000, max_steps=1000, gamma=0.99):
        for i in range(max_episodes):
            obs = self.env.reset()
            done = False
            total_reward = 0
            for t in range(max_steps):
                # 选择动作
                action_probs = self.actor(torch.FloatTensor(obs))
                action = torch.multinomial(action_probs, 1).item()

                # 执行动作
                next_obs, reward, done, _ = self.env.step(action)

                # 更新Critic
                value = self.critic(torch.FloatTensor(obs)).item()
                next_value = self.critic(torch.FloatTensor(next_obs)).item()
                td_error = reward + gamma * next_value - value
                critic_loss = td_error ** 2
                self.critic_optimizer.zero_grad()
                critic_loss.backward()
                self.critic_optimizer.step()

                # 更新Actor
                advantage = td_error
                log_prob = torch.log(action_probs[action])
                actor_loss = -log_prob * advantage
                self.actor_optimizer.zero_grad()
                actor_loss.backward()
                self.actor_optimizer.step()

                obs = next_obs
                total_reward += reward
                if done:
                    break

            print("Episode {}: Total reward = {}".format(i, total_reward))

# 使用CartPole环境测试Actor-Critic算法
env = gym.make('CartPole-v0')
ac = ActorCritic(env)
ac.train()