Pytorch多智能体DQN代码

以下是一个简单的PyTorch多智能体DQN代码示例：

import random
import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim


class DQNAgent(nn.Module):
    def __init__(self, state_size, action_size, num_agents):
        super(DQNAgent, self).__init__()
        self.state_size = state_size
        self.action_size = action_size
        self.num_agents = num_agents
        self.memory = []
        self.gamma = 0.99
        self.epsilon = 1.0
        self.epsilon_decay = 0.995
        self.epsilon_min = 0.01
        self.learning_rate = 0.001
        self.fc1 = nn.Linear(self.state_size, 24)
        self.fc2 = nn.Linear(24, 24)
        self.fc3 = nn.Linear(24, self.action_size)
        self.optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)

    def forward(self, state):
        x = torch.relu(self.fc1(state))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def remember(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return [random.randrange(self.action_size) for _ in range(self.num_agents)]
        state = torch.FloatTensor(state)
        act_values = self.forward(state)
        return [np.argmax(act_values[i].detach().numpy()) for i in range(self.num_agents)]

    def replay(self, batch_size):
        if len(self.memory) < batch_size:
            return
        minibatch = random.sample(self.memory, batch_size)
        states = torch.FloatTensor([minibatch[i][0] for i in range(batch_size)])
        actions = torch.LongTensor([minibatch[i][1] for i in range(batch_size)])
        rewards = torch.FloatTensor([minibatch[i][2] for i in range(batch_size)])
        next_states = torch.FloatTensor([minibatch[i][3] for i in range(batch_size)])
        dones = torch.FloatTensor([minibatch[i][4] for i in range(batch_size)])
        q_values = self.forward(states)
        next_q_values = self.forward(next_states)
        target_q_values = q_values.clone().detach()
        for i in range(batch_size):
            target = rewards[i]
            if not dones[i]:
                target += self.gamma * torch.max(next_q_values[i]).item()
            target_q_values[i][actions[i]] = target
        loss = nn.functional.mse_loss(q_values, target_q_values)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()
        if self.epsilon > self.epsilon_min:
            self.epsilon *= self.epsilon_decay

在这个示例中，`DQNAgent` 类定义了一个多智能体的DQN代理。它是一个 `nn.Module`，定义了一个具有两个隐藏层的神经网络模型，用于近似每个智能体在给定状态下采取每个动作的Q值。`remember` 方法用于将每个智能体的经验存储在内存中。`act` 方法用于根据当前状态选择动作，epsilon-greedy策略用于在探索和利用之间进行权衡。`replay` 方法用于从经验回放缓冲区中随机抽取一批经验，然后使用DQN算法来更新神经网络模型的权重。`replay` 方法还用于逐渐降低探索率。在每次训练时，我们使用 `nn.functional.mse_loss` 计算损失，然后使用 `optimizer.step()` 更新模型权重。