深度 Qlearning：在智能医疗诊断中的应用

    import numpy as np
    import tensorflow as tf
    from tensorflow import keras
    from collections import deque
    import random
    
      
      
      
      
    
    代码解读

    class DQN:
    def __init__(self, state_size, action_size, learning_rate, gamma, epsilon, epsilon_decay, epsilon_min, batch_size, memory_size):
        self.state_size = state_size
        self.action_size = action_size
        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.memory = deque(maxlen=memory_size)
    
        self.model = self.build_model()
        self.target_model = self.build_model()
        self.update_target_model()
    
    def build_model(self):
        model = keras.Sequential([
            keras.layers.Dense(64, input_shape=(self.state_size,), activation='relu'),
            keras.layers.Dense(64, activation='relu'),
            keras.layers.Dense(self.action_size, activation='linear')
        ])
        model.compile(loss='mse', optimizer=keras.optimizers.Adam(lr=self.learning_rate))
        return model
    
    def update_target_model(self):
        self.target_model.set_weights(self.model.get_weights())
    
    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)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0])
    
    def replay(self):
        if len(self.memory) < self.batch_size:
            return
        minibatch = random.sample(self.memory, self.batch_size)
        states, actions, rewards, next_states, dones = zip(*minibatch)
        states = np.array(states)
        actions = np.array(actions)
        rewards = np.array(rewards)
        next_states = np.array(next_states)
        dones = np.array(dones)
    
        targets = rewards + (1 - dones) * self.gamma * np.amax(self.target_model.predict(next_states), axis=1)
        targets_full = self.model.predict(states)
        targets_full[np.arange(self.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
    
    def load(self, name):
        self.model.load_weights(name)
    
    def save(self, name):
        self.model.save_weights(name)
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def train(env, agent, episodes, max_steps, update_target_freq):
    rewards = []
    for episode in range(episodes):
        state = env.reset()
        state = np.reshape(state, [1, agent.state_size])
        total_reward = 0
        for step in range(max_steps):
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            next_state = np.reshape(next_state, [1, agent.state_size])
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            total_reward += reward
            if done:
                break
        rewards.append(total_reward)
        agent.replay()
        if episode % update_target_freq == 0:
            agent.update_target_model()
        print(f"Episode: {episode+1}/{episodes}, Reward: {total_reward}, Epsilon: {agent.epsilon:.2}")
    return rewards
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读

    def test(env, agent, episodes, max_steps):
    rewards = []
    for episode in range(episodes):
        state = env.reset()
        state = np.reshape(state, [1, agent.state_size])
        total_reward = 0
        for step in range(max_steps):
            action = np.argmax(agent.model.predict(state)[0])
            next_state, reward, done, _ = env.step(action)
            next_state = np.reshape(next_state, [1, agent.state_size])
            state = next_state
            total_reward += reward
            if done:
                break
        rewards.append(total_reward)
        print(f"Episode: {episode+1}/{episodes}, Reward: {total_reward}")
    return rewards
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    
    代码解读