深度 Qlearning：在机器人技术中的应用

    import gym
    import numpy as np
    import tensorflow as tf
    
    # 定义深度 Q-learning 网络
    class DQN(tf.keras.Model):
    def __init__(self, state_dim, action_dim):
        super(DQN, self).__init__()
        self.dense1 = tf.keras.layers.Dense(64, activation='relu')
        self.dense2 = tf.keras.layers.Dense(64, activation='relu')
        self.dense3 = tf.keras.layers.Dense(action_dim)
    
    def call(self, state):
        x = self.dense1(state)
        x = self.dense2(x)
        return self.dense3(x)
    
    # 定义经验回放缓冲区
    class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = []
        self.capacity = capacity
        self.position = 0
    
    def push(self, state, action, reward, next_state, done):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = (state, action, reward, next_state, done)
        self.position = (self.position + 1) % self.capacity
    
    def sample(self, batch_size):
        batch = random.sample(self.buffer, batch_size)
        state, action, reward, next_state, done = map(np.stack, zip(*batch))
        return state, action, reward, next_state, done
    
    # 定义深度 Q-learning 智能体
    class DQNAgent:
    def __init__(self, state_dim, action_dim, learning_rate=1e-3, gamma=0.99, epsilon=1.0, epsilon_decay=0.995, epsilon_min=0.01, batch_size=32, buffer_capacity=10000):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.learning_rate = learning_rate
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_decay = epsilon_decay
        self.epsilon_min = epsilon_min
        self.batch_size = batch_size
        self.buffer = ReplayBuffer(buffer_capacity)
    
        # 创建深度 Q-learning 网络和目标网络
        self.dqn = DQN(state_dim, action_dim)
        self.target_dqn = DQN(state_dim, action_dim)
        self.optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
    
    def act(self, state):
        if np.random.rand() <= self.epsilon:
            return np.random.choice(self.action_dim)
        else:
            q_values = self.dqn(state[np.newaxis, :])
            return np.argmax(q_values.numpy()[0])
    
    def train(self):
        if len(self.buffer.buffer) < self.batch_size:
            return
    
        state, action, reward, next_state, done = self.buffer.sample(self.batch_size)
    
        # 计算目标 Q 值
        target_q_values = self.target_dqn(next_state)
        max_target_q_values = tf.reduce_max(target_q_values, axis=1)
        target_q_values = reward + self.gamma * max_target_q_values * (1 - done)
    
        # 计算损失函数并更新网络参数
        with tf.GradientTape() as tape:
            q_values = self.dqn(state)
            action_one_hot = tf.one_hot(action, self.action_dim)
            q_value = tf.reduce_sum(q_values * action_one_hot, axis=1)
            loss = tf.keras.losses.mse(target_q_values, q_value)
    
        gradients = tape.gradient(loss, self.dqn.trainable_variables)
        self.optimizer.apply_gradients(zip(gradients, self.dqn.trainable_variables))
    
        # 更新 epsilon
        self.epsilon = max(self.epsilon * self.epsilon_decay, self.epsilon_min)
    
    def update_target_network(self):
        self.target_dqn.set_weights(self.dqn.get_weights())
    
    # 创建 CartPole 环境
    env = gym.make('CartPole-v1')
    
    # 获取状态和动作维度
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    
    # 创建 DQN 智能体
    agent = DQNAgent(state_dim, action_dim)
    
    # 训练智能体
    for episode in range(1000):
    state = env.reset()
    done = False
    total_reward = 0
    
    while not done:
        action = agent.act(state)
        next_state, reward, done, _ = env.step(action)
        agent.buffer.push(state, action, reward, next_state, done)
        agent.train()
        total_reward += reward
        state = next_state
    
    # 每 10 个 episode 更新目标网络
    if episode % 10 == 0:
        agent.update_target_network()
    
    print('Episode: {}, Total Reward: {}'.format(episode, total_reward))
    
    # 测试训练好的智能体
    state = env.reset()
    done = False
    total_reward = 0
    
    while not done:
    action = agent.act(state)
    next_state, reward, done, _ = env.step(action)
    total_reward += reward
    state = next_state
    
    print('Test Total Reward: {}'.format(total_reward))
    
    # 关闭环境
    env.close()
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读