编写MADDPG代码，适用于有两个不同奖励函数的智能体，且它们的动作空间是离散的场景

以下是适用于有两个不同奖励函数的智能体，且它们的动作空间是离散的MADDPG代码示例：

import numpy as np
import torch
import torch.nn.functional as F
from torch.optim import Adam
from collections import deque
import random

# 定义Actor网络
class ActorNet(torch.nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        super(ActorNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = torch.nn.Linear(hidden_dim, action_dim)
        
    def forward(self, x):
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = F.softmax(self.fc3(x), dim=1)
        return x

# 定义Critic网络
class CriticNet(torch.nn.Module):
    def __init__(self, state_dim, action_dim, hidden_dim):
        super(CriticNet, self).__init__()
        self.fc1 = torch.nn.Linear(state_dim+action_dim, hidden_dim)
        self.fc2 = torch.nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = torch.nn.Linear(hidden_dim, 1)
        
    def forward(self, state, action):
        x = torch.cat([state, action], dim=1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 定义智能体
class MADDPG:
    def __init__(self, state_dim, action_dim, hidden_dim, lr):
        self.n_agents = 2
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim
        self.lr = lr
        
        # 初始化Actor网络和Critic网络
        self.actor_nets = [ActorNet(state_dim, action_dim, hidden_dim) for _ in range(self.n_agents)]
        self.critic_nets = [CriticNet(state_dim*self.n_agents, action_dim*self.n_agents, hidden_dim) for _ in range(self.n_agents)]
        self.target_actor_nets = [ActorNet(state_dim, action_dim, hidden_dim) for _ in range(self.n_agents)]
        self.target_critic_nets = [CriticNet(state_dim*self.n_agents, action_dim*self.n_agents, hidden_dim) for _ in range(self.n_agents)]
        
        # 初始化优化器
        self.actor_optimizers = [Adam(actor_net.parameters(), lr=self.lr) for actor_net in self.actor_nets]
        self.critic_optimizers = [Adam(critic_net.parameters(), lr=self.lr) for critic_net in self.critic_nets]
        
        # 初始化经验池
        self.memory = deque(maxlen=10000)
        
        # 初始化超参数
        self.batch_size = 128
        self.gamma = 0.95
        self.tau = 0.01
        
    # 选择动作
    def select_action(self, state, agent_id):
        state = torch.FloatTensor(state).unsqueeze(0)
        action_probs = self.actor_nets[agent_id](state)
        action = np.random.choice(self.action_dim, p=action_probs.detach().numpy()[0])
        return action
    
    # 添加经验
    def add_experience(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))
    
    # 更新网络
    def update(self):
        # 从经验池中获取样本
        if len(self.memory) < self.batch_size:
            return
        batch = random.sample(self.memory, self.batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        states = torch.FloatTensor(states)
        actions = torch.LongTensor(actions).view(self.batch_size, -1)
        rewards = torch.FloatTensor(rewards).view(self.batch_size, -1)
        next_states = torch.FloatTensor(next_states)
        dones = torch.FloatTensor(dones).view(self.batch_size, -1)
        
        # 计算每个智能体的目标值
        target_actions = []
        for i in range(self.n_agents):
            target_action_probs = self.target_actor_nets[i](next_states)
            target_action = torch.argmax(target_action_probs, dim=1, keepdim=True)
            target_actions.append(target_action)
        target_actions = torch.cat(target_actions, dim=1)
        target_qs = []
        for i in range(self.n_agents):
            target_qs.append(self.target_critic_nets[i](next_states, target_actions))
        target_qs = torch.cat(target_qs, dim=1)
        targets = rewards + self.gamma * target_qs * (1 - dones)
        
        # 更新Critic网络
        for i in range(self.n_agents):
            self.critic_optimizers[i].zero_grad()
            q = self.critic_nets[i](states, actions)
            critic_loss = F.mse_loss(q, targets.detach())
            critic_loss.backward()
            self.critic_optimizers[i].step()
        
        # 更新Actor网络
        for i in range(self.n_agents):
            self.actor_optimizers[i].zero_grad()
            actions_probs = self.actor_nets[i](states)
            actions_probs = actions_probs.detach()
            actions_probs[:, actions[:, i]] = F.softmax(actions_probs[:, actions[:, i]], dim=1)
            q_values = self.critic_nets[i](states, actions_probs)
            actor_loss = -q_values.mean()
            actor_loss.backward()
            self.actor_optimizers[i].step()
            
        # 更新目标网络
        for i in range(self.n_agents):
            for target_param, param in zip(self.target_actor_nets[i].parameters(), self.actor_nets[i].parameters()):
                target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
            for target_param, param in zip(self.target_critic_nets[i].parameters(), self.critic_nets[i].parameters()):
                target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)

在这个例子中，我们定义了两个Actor网络和两个Critic网络，每个智能体都有一个Actor和一个Critic。其中，Actor网络的输出是一个概率分布，用于确定智能体的行动；Critic网络的输出是估计的Q值，用于评估智能体的行动。在训练过程中，我们使用经验重放缓存器来存储智能体的经验，然后从缓存器中随机选择一批经验进行训练。在更新Critic网络时，我们使用均方误差作为损失函数；在更新Actor网络时，我们使用Critic网络的输出作为损失函数，并采用随机梯度下降法进行优化。为了避免过度拟合，我们使用了目标网络进行训练，即每个智能体都有一个目标Actor网络和一个目标Critic网络。在每次更新后，我们使用软更新法来更新目标网络的参数。