多智能体DQN代码

以下是一个简单的多智能体DQN代码示例：

import random
import numpy as np
import tensorflow as tf


class DQNAgent:
    def __init__(self, state_size, action_size, num_agents):
        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.model = self._build_model()

    def _build_model(self):
        model = tf.keras.Sequential()
        model.add(tf.keras.layers.Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(tf.keras.layers.Dense(24, activation='relu'))
        model.add(tf.keras.layers.Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=self.learning_rate))
        return model

    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)]
        act_values = self.model.predict(np.array(state))
        return [np.argmax(act_values[i]) 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 = np.array([minibatch[i][0] for i in range(batch_size)])
        actions = np.array([minibatch[i][1] for i in range(batch_size)])
        rewards = np.array([minibatch[i][2] for i in range(batch_size)])
        next_states = np.array([minibatch[i][3] for i in range(batch_size)])
        dones = np.array([minibatch[i][4] for i in range(batch_size)])
        targets = rewards + self.gamma * np.amax(self.model.predict(next_states), axis=1) * (1 - dones)
        targets_full = self.model.predict(states)
        targets_full[np.arange(batch_size), actions] = targets
        self.model.fit(states, targets_full, epochs=1, verbose=0)
        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。`_build_model` 方法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。