一切皆是映射：DQN在医疗诊断中的应用：挑战与机遇

    import gym
    import numpy as np
    import tensorflow as tf
    
    # 定义环境
    env = gym.make('CartPole-v0')
    
    # 定义 DQN 模型
    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)
    
    # 定义经验回放机制
    class ReplayBuffer:
    def __init__(self, capacity):
        self.buffer = []
        self.capacity = capacity
        self.position = 0
    
    def push(self, experience):
        if len(self.buffer) < self.capacity:
            self.buffer.append(None)
        self.buffer[self.position] = experience
        self.position = (self.position + 1) % self.capacity
    
    def sample(self, batch_size):
        return random.sample(self.buffer, batch_size)
    
    # 定义 DQN 算法
    class DQNAgent:
    def __init__(self, state_dim, action_dim, learning_rate, gamma, epsilon):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.learning_rate = learning_rate
        self.gamma = gamma
        self.epsilon = epsilon
        self.q_network = DQN(state_dim, action_dim)
        self.target_q_network = DQN(state_dim, action_dim)
        self.optimizer = tf.keras.optimizers.Adam(learning_rate)
        self.replay_buffer = ReplayBuffer(10000)
    
    def choose_action(self, state):
        if np.random.rand() < self.epsilon:
            return np.random.choice(self.action_dim)
        else:
            q_values = self.q_network(state[np.newaxis, :])
            return np.argmax(q_values)
    
    def train(self, batch_size):
        if len(self.replay_buffer.buffer) < batch_size:
            return
        batch = self.replay_buffer.sample(batch_size)
        states, actions, rewards, next_states, dones = zip(*batch)
        states = np.array(states)
        actions = np.array(actions)
        rewards = np.array(rewards)
        next_states = np.array(next_states)
        dones = np.array(dones)
    
        with tf.GradientTape() as tape:
            q_values = self.q_network(states)
            next_q_values = self.target_q_network(next_states)
            target_q_values = rewards + self.gamma * tf.reduce_max(next_q_values, axis=1) * (1 - dones)
            q_values = tf.gather_nd(q_values, tf.stack([tf.range(batch_size), actions], axis=1))
            loss = tf.reduce_mean(tf.square(target_q_values - q_values))
    
        grads = tape.gradient(loss, self.q_network.trainable_variables)
        self.optimizer.apply_gradients(zip(grads, self.q_network.trainable_variables))
    
    def update_target_network(self):
        self.target_q_network.set_weights(self.q_network.get_weights())
    
    # 初始化 DQN agent
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.n
    learning_rate = 0.001
    gamma = 0.99
    epsilon = 0.1
    agent = DQNAgent(state_dim, action_dim, learning_rate, gamma, epsilon)
    
    # 训练 DQN agent
    for episode in range(1000):
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
        action = agent.choose_action(state)
        next_state, reward, done, _ = env.step(action)
        agent.replay_buffer.push((state, action, reward, next_state, done))
        agent.train(32)
        state = next_state
        total_reward += reward
    agent.update_target_network()
    print('Episode: {}, Total Reward: {}'.format(episode, total_reward))
    
    # 测试 DQN agent
    state = env.reset()
    done = False
    total_reward = 0
    while not done:
    action = agent.choose_action(state)
    next_state, reward, done, _ = env.step(action)
    state = next_state
    total_reward += reward
    print('Total Reward: {}'.format(total_reward))
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
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