深度 Qlearning：在物联网系统中的应用

    import tensorflow as tf
    import numpy as np
    
    # 定义环境
    class Environment:
    def __init__(self):
        # 初始化迷宫
        self.maze = np.array([
            [0, 0, 0, 1],
            [0, 1, 0, 0],
            [0, 0, 0, 0],
            [0, 0, 1, 0]
        ])
        # 设置目标位置
        self.goal = (3, 3)
        # 初始化智能体位置
        self.agent_pos = (0, 0)
    
    def reset(self):
        # 重置智能体位置
        self.agent_pos = (0, 0)
        # 返回初始状态
        return self.agent_pos
    
    def step(self, action):
        # 定义行动
        actions = {
            0: (-1, 0),  # 上
            1: (1, 0),   # 下
            2: (0, -1),  # 左
            3: (0, 1)   # 右
        }
        # 移动智能体
        new_pos = (self.agent_pos[0] + actions[action][0],
                   self.agent_pos[1] + actions[action][1])
        # 检查是否撞墙
        if 0 <= new_pos[0] < self.maze.shape[0] and \
           0 <= new_pos[1] < self.maze.shape[1] and \
           self.maze[new_pos] == 0:
            self.agent_pos = new_pos
        # 计算奖励
        if self.agent_pos == self.goal:
            reward = 1
        elif self.maze[self.agent_pos] == 1:
            reward = -1
        else:
            reward = 0
        # 返回下一个状态、奖励和是否结束
        return self.agent_pos, reward, self.agent_pos == self.goal
    
    # 定义深度 Q 网络
    class DQN:
    def __init__(self, state_dim, action_dim):
        self.state_dim = state_dim
        self.action_dim = action_dim
        # 创建 Q 网络
        self.q_network = self.create_q_network()
        # 创建目标网络
        self.target_network = self.create_q_network()
        # 初始化目标网络参数
        self.target_network.set_weights(self.q_network.get_weights())
    
    def create_q_network(self):
        # 创建一个简单的 MLP
        model = tf.keras.models.Sequential([
            tf.keras.layers.Dense(32, activation='relu', input_shape=(self.state_dim,)),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(self.action_dim)
        ])
        # 编译模型
        model.compile(loss='mse', optimizer=tf.keras.optimizers.Adam(lr=0.001))
        return model
    
    def predict(self, state):
        # 预测 Q 值
        return self.q_network.predict(state)
    
    def train(self, states, actions, rewards, next_states, dones):
        # 计算目标 Q 值
        target_q_values = self.target_network.predict(next_states)
        # 计算 TD 目标
        targets = rewards + (1 - dones) * 0.9 * np.max(target_q_values, axis=1)
        # 创建掩码
        masks = tf.one_hot(actions, self.action_dim)
        with tf.GradientTape() as tape:
            # 计算 Q 值
            q_values = self.q_network(states)
            # 计算 Q 值的掩码版本
            masked_q_values = tf.reduce_sum(tf.multiply(q_values, masks), axis=1)
            # 计算损失
            loss = tf.reduce_mean(tf.square(targets - masked_q_values))
        # 计算梯度
        grads = tape.gradient(loss, self.q_network.trainable_variables)
        # 更新 Q 网络参数
        self.q_network.optimizer.apply_gradients(zip(grads, self.q_network.trainable_variables))
    
    def update_target_network(self):
        # 更新目标网络参数
        self.target_network.set_weights(self.q_network.get_weights())
    
    # 创建环境和 DQN
    env = Environment()
    dqn = DQN(state_dim=2, action_dim=4)
    
    # 设置超参数
    num_episodes = 1000
    epsilon = 0.1
    batch_size = 32
    target_update_freq = 100
    
    # 训练循环
    for episode in range(num_episodes):
    # 重置环境
    state = env.reset()
    # 初始化总奖励
    total_reward = 0
    # 循环直到结束
    done = False
    while not done:
        # 选择行动
        if np.random.rand() < epsilon:
            action = np.random.randint(4)
        else:
            q_values = dqn.predict(np.array([state]))[0]
            action = np.argmax(q_values)
        # 执行行动
        next_state, reward, done = env.step(action)
        # 存储经验
        memory.append((state, action, reward, next_state, done))
        # 更新状态
        state = next_state
        # 更新总奖励
        total_reward += reward
        # 训练 DQN
        if len(memory) >= batch_size:
            batch = random.sample(memory, batch_size)
            states, actions, rewards, next_states, dones = zip(*batch)
            dqn.train(np.array(states), np.array(actions), np.array(rewards),
                      np.array(next_states), np.array(dones))
        # 更新目标网络
        if episode % target_update_freq == 0:
            dqn.update_target_network()
    # 打印结果
    print(f'Episode {episode + 1}, Total reward: {total_reward}')