深度q网络对八分类数据进行强化学习代码

以下是使用深度 Q 网络（DQN）对八分类数据进行强化学习的 Python 代码示例：

import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

class DQNAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.memory = []
        self.gamma = 0.95 # discount rate
        self.epsilon = 1.0 # exploration rate
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.001
        self.model = self._build_model()

    def _build_model(self):
        # Neural Network for Deep Q Learning
        model = Sequential()
        model.add(Dense(24, input_dim=self.state_size, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_size, activation='linear'))
        model.compile(loss='mse', optimizer=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)
        act_values = self.model.predict(state)
        return np.argmax(act_values[0]) 

    def replay(self, batch_size):
        minibatch = random.sample(self.memory, batch_size)
        for state, action, reward, next_state, done in minibatch:
            target = reward
            if not done:
                target = (reward + self.gamma *
                          np.amax(self.model.predict(next_state)[0]))
            target_f = self.model.predict(state)
            target_f[0][action] = target
            self.model.fit(state, target_f, 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)

# main function
if __name__ == "__main__":
    # initialize gym environment and the agent
    state_size = 8
    action_size = 8
    agent = DQNAgent(state_size, action_size)

    # train DQN agent
    batch_size = 32
    episodes = 1000
    for e in range(episodes):
        state = np.random.rand(1, state_size)
        for time in range(500):
            action = agent.act(state)
            next_state = np.random.rand(1, state_size)
            reward = np.random.randint(0, 2)
            done = False
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            if done:
                print("episode: {}/{}, score: {}, e: {:.2}"
                      .format(e, episodes, time, agent.epsilon))
                break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)

    # save trained model weights
    agent.save("model.h5")

以上代码示例中，采用了 Keras 框架搭建了一个具有两个隐藏层的神经网络模型，并使用 Q-learning 算法进行训练。在训练过程中，每个时间步，根据当前状态选择一个动作并执行，然后通过环境返回一个奖励信号以及下一个状态。将这些信息存储在记忆库中，每次从记忆库中随机抽取一批数据进行训练，直到达到一定的训练次数或准确率。最后保存训练好的模型权重，以备后续使用。