强化学习中的q-learing和深度学习结合的代码

强化学习中的Q-learning算法是一种基于计算打分的学习方法，可以优化决策和行动。将Q-learning算法与深度学习相结合可以提高模型的性能和准确度。

一种常见的结合方式是使用深度神经网络（DNN）作为Q-table的替代品来保存Q-values，这种方法被称为Deep Q-Networks(DQN)。DQN将状态空间映射到一个高维空间中，并且使用神经网络的高容量、非线性及端到端学习来获得更准确的Q-values。

下面是一个简单的DQN的实现:

1. 首先是定义神经网络模型

class DeepQNetwork:
    def __init__(self, observation_space, action_space, learning_rate=0.01, 
                 gamma=0.9, epsilon=0.9, epsilon_min=0.01, epsilon_decay=0.995):
        self.observation_space = observation_space
        self.action_space = action_space
        self.learning_rate = learning_rate
        self.gamma = gamma
        self.epsilon = epsilon
        self.epsilon_min = epsilon_min
        self.epsilon_decay = epsilon_decay
        self.memory = deque(maxlen=2000)
        self.model = self.build_model()

    def build_model(self):
        model = Sequential()
        model.add(Dense(24, input_dim=self.observation_space, activation='relu'))
        model.add(Dense(24, activation='relu'))
        model.add(Dense(self.action_space, activation='linear'))
        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))
        return model

2.定义算法过程

def run_dqn(agent, env, episodes=1000, batch_size=32):
    scores = []
    for ep in range(episodes):
        state = env.reset()
        score = 0
        for time_step in range(500):
            action = agent.act(state)
            next_state, reward, done, info = env.step(action)
            agent.remember(state, action, reward, next_state, done)
            if len(agent.memory) > batch_size:
                agent.replay(batch_size)
            score += reward
            state = next_state
            if done:
                break
        agent.update_epsilon_decay()
        scores.append(score)
        print('Episode: {} Score: {} Epsilon: {:.4f}'.format(ep,score, agent.epsilon))
    return scores

3. 定义训练过程

dqn_agent = DeepQNetwork(env.observation_space.shape[0], env.action_space.n,)
scores = run_dqn(dqn_agent, env)

通过以上代码实现了深度学习和Q-learning的结合，将两种算法相辅相成，取长补短，创造出更高效准确的算法模型。