dqn的python代码，用中午注解

很高兴为您解答这个问题。以下是 Deep Q-Network（DQN）的Python代码，带有中文注释说明：

import gym
import numpy as np
import tensorflow as tf
from tensorflow import keras

# 设置超参数
ENV_NAME = "CartPole-v0"
MEMORY_SIZE = 100000  # 记忆池大小
BATCH_SIZE = 32  # 批量大小
GAMMA = 0.99  # 折扣因子
EPSILON_START = 1.0  # 初始探索率
EPSILON_MIN = 0.01  # 最低探索率
EPSILON_DECAY = 1000000  # 探索率衰减率
TARGET_UPDATE_FREQ = 1000  # 目标网络更新频率

# 创建环境
env = gym.make(ENV_NAME)
num_actions = env.action_space.n

# 创建Q网络和目标网络
q_net = keras.Sequential()
q_net.add(keras.layers.Dense(24, input_shape=env.observation_space.shape, activation="relu"))
q_net.add(keras.layers.Dense(24, activation="relu"))
q_net.add(keras.layers.Dense(num_actions, activation=None))

target_net = keras.models.clone_model(q_net)
target_net.set_weights(q_net.get_weights())

# 创建记忆池
memory_states = np.zeros((MEMORY_SIZE, env.observation_space.shape[0]))
memory_actions = np.zeros((MEMORY_SIZE,), dtype=np.uint8)
memory_rewards = np.zeros((MEMORY_SIZE,))
memory_next_states = np.zeros((MEMORY_SIZE, env.observation_space.shape[0]))
memory_dones = np.zeros((MEMORY_SIZE,), dtype=np.uint8)
memory_counter = 0

# 初始化探索率和步数计数器
epsilon = EPSILON_START
step_count = 0

# 定义损失函数和优化器
loss_func = keras.losses.mean_squared_error
optimizer = keras.optimizers.Adam(learning_rate=0.001)

# 训练Q网络
for episode in range(1000):
    state = env.reset()
    episode_reward = 0
    done = False
    while not done:
        # 探索或利用
        if np.random.rand() < epsilon:
            action = env.action_space.sample()
        else:
            q_values = q_net.predict(np.expand_dims(state, axis=0))
            action = np.argmax(q_values)
        
        # 执行动作，得到下一个状态、奖励和是否结束标志
        next_state, reward, done, _ = env.step(action)
        episode_reward += reward
        
        # 将记忆添加到记忆池中
        memory_states[memory_counter] = state
        memory_actions[memory_counter] = action
        memory_rewards[memory_counter] = reward
        memory_next_states[memory_counter] = next_state
        memory_dones[memory_counter] = done
        memory_counter = (memory_counter + 1) % MEMORY_SIZE
        
        # 更新Q网络
        if memory_counter >= BATCH_SIZE:
            batch_indices = np.random.choice(MEMORY_SIZE, BATCH_SIZE, replace=False)
            batch_states = memory_states[batch_indices]
            batch_actions = memory_actions[batch_indices]
            batch_rewards = memory_rewards[batch_indices]
            batch_next_states = memory_next_states[batch_indices]
            batch_dones = memory_dones[batch_indices]
            
            q_values_next = target_net.predict_on_batch(batch_next_states)
            max_q_values_next = np.max(q_values_next, axis=1)
            targets = batch_rewards + (1 - batch_dones) * GAMMA * max_q_values_next
            
            q_values = q_net.predict_on_batch(batch_states)
            q_values[np.arange(BATCH_SIZE), batch_actions] = targets
            
            loss = q_net.train_on_batch(batch_states, q_values)
        
        # 更新目标网络
        if step_count % TARGET_UPDATE_FREQ == 0:
            target_net.set_weights(q_net.get_weights())
        
        # 更新探索率和步数计数器
        epsilon = max(EPSILON_MIN, EPSILON_START - step_count / EPSILON_DECAY)
        step_count += 1
        
        # 进入下一个状态
        state = next_state
    
    print(f"Episode {episode + 1}: reward = {episode_reward}")

这是一个简单的DQN实现，用于解决OpenAI Gym的CartPole环境。它使用TensorFlow 2.0构建神经网络，基于经验回放和固定Q目标的思想进行训练。在训练过程中，会逐渐降低探索率，提高利用率，从而使得智能体更加稳定地学习到正确的行为策略。