帮我写一段DQN代码示例，并且逐行注释

好的，以下是一个简单的DQN代码示例，并逐行注释：

import gym
import random
import numpy as np
from collections import deque
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 = deque(maxlen=2000)    # 定义记忆库，长度为2000
        self.gamma = 0.95                   # 折扣因子
        self.epsilon = 1.0                  # 探索率
        self.epsilon_min = 0.01             # 探索率的下限
        self.epsilon_decay = 0.995          # 探索率的衰减率
        self.learning_rate = 0.001          # 学习率
        self.model = self._build_model()    # 定义神经网络模型

    def _build_model(self):
        # 定义一个三层的全连接神经网络模型
        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):
        # 将当前状态、动作、奖励、下一个状态和done标识存储到记忆库中
        self.memory.append((state, action, reward, next_state, done))

    def act(self, state):
        # 以一定概率随机选择动作，或者根据当前状态选择最优动作
        if np.random.rand() <= self.epsilon:
            return random.randrange(self.action_size)
        else:
            return np.argmax(self.model.predict(state)[0])

    def replay(self, batch_size):
        # 从记忆库中随机抽取一批数据进行训练
        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):
        # 加载神经网络模型的权重
        self.model.load_weights(name)

    def save(self, name):
        # 保存神经网络模型的权重
        self.model.save_weights(name)

if __name__ == "__main__":
    # 创建CartPole-v1环境
    env = gym.make('CartPole-v1')
    state_size = env.observation_space.shape[0]
    action_size = env.action_space.n
    agent = DQNAgent(state_size, action_size)
    done = False
    batch_size = 32

    for e in range(1000):
        state = env.reset()
        state = np.reshape(state, [1, state_size])
        for time in range(500):
            env.render()
            action = agent.act(state)
            next_state, reward, done, _ = env.step(action)
            reward = reward if not done else -10
            next_state = np.reshape(next_state, [1, state_size])
            agent.remember(state, action, reward, next_state, done)
            state = next_state
            if done:
                print("episode: {}/{}, score: {}, e: {:.2}"
                      .format(e, 1000, time, agent.epsilon))
                break
        if len(agent.memory) > batch_size:
            agent.replay(batch_size)
        if e % 50 == 0:
            agent.save("cartpole-dqn.h5")