提供一段强化学习AC算法代码

以下是使用Python编写的简单强化学习Actor-Critic（AC）算法代码示例：

import gym
import numpy as np
import tensorflow as tf

# 设置超参数
num_episodes = 1000
learning_rate = 0.01
discount_factor = 0.99

# 初始化环境和神经网络模型
env = gym.make('CartPole-v0')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
actor_model = tf.keras.Sequential([
    tf.keras.layers.Dense(24, input_shape=(state_size,), activation='relu'),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(action_size, activation='softmax')
])
critic_model = tf.keras.Sequential([
    tf.keras.layers.Dense(24, input_shape=(state_size,), activation='relu'),
    tf.keras.layers.Dense(24, activation='relu'),
    tf.keras.layers.Dense(1, activation='linear')
])
actor_optimizer = tf.keras.optimizers.Adam(learning_rate)
critic_optimizer = tf.keras.optimizers.Adam(learning_rate)

# 训练AC算法
for episode in range(num_episodes):
    state = env.reset()
    total_reward = 0
    done = False
    while not done:
        # 从Actor策略中选择动作
        action_probs = actor_model(np.array([state]))
        action = np.random.choice(action_size, p=action_probs.numpy()[0])
        
        # 执行动作并更新环境状态
        next_state, reward, done, _ = env.step(action)
        total_reward += reward
        
        # 计算Critic的TD误差并更新参数
        next_value = critic_model(np.array([next_state])).numpy()[0]
        target_value = reward + discount_factor * next_value
        with tf.GradientTape() as tape:
            value = critic_model(np.array([state]))
            td_error = tf.math.square(target_value - value)
        critic_grads = tape.gradient(td_error, critic_model.trainable_variables)
        critic_optimizer.apply_gradients(zip(critic_grads, critic_model.trainable_variables))
        
        # 计算Actor的梯度并更新参数
        with tf.GradientTape() as tape:
            log_probs = tf.math.log(action_probs[0, action])
            advantage = target_value - value
            actor_loss = -log_probs * advantage
        actor_grads = tape.gradient(actor_loss, actor_model.trainable_variables)
        actor_optimizer.apply_gradients(zip(actor_grads, actor_model.trainable_variables))
        
        state = next_state
    
    # 输出训练结果
    print('Episode: {}, Total Reward: {}'.format(episode, total_reward))

这段代码使用了TensorFlow和OpenAI Gym库。其中，`actor_model`和`critic_model`分别代表Actor和Critic神经网络模型，`actor_optimizer`和`critic_optimizer`分别代表Actor和Critic的优化器，`num_episodes`表示训练的总轮数，`learning_rate`表示学习率，`discount_factor`表示折扣因子。在每一轮训练中，首先使用Actor模型选择一个动作，然后执行该动作并更新环境状态。接着，根据Critic模型估计下一个状态的价值，并计算TD误差和优势函数。最后，使用TD误差和优