请采用python基于Tensorflow架构提供基于最大熵的SAC强化学习算法代码

以下是基于TensorFlow构建的最大熵SAC强化学习算法的代码示例：

import tensorflow as tf
import numpy as np
import gym

# 定义常量
LR_A = 0.001  # actor的learning rate
LR_C = 0.002  # critic的learning rate
GAMMA = 0.9  # 折扣因子
TAU = 0.01  # 软更新的参数
MEMORY_CAPACITY = 10000  # 记忆库的大小
BATCH_SIZE = 32  # 批处理的大小
MAX_EPISODES = 200  # 最大的训练次数
MAX_EP_STEPS = 200  # 每次训练步数的最大值

# 定义Actor网络
class Actor(object):
    def __init__(self, sess, n_features, n_actions, lr=0.001):
        self.sess = sess
        self.s = tf.placeholder(tf.float32, [None, n_features], 'state')
        self.a = tf.placeholder(tf.float32, [None, n_actions], 'action')
        self.td_error = tf.placeholder(tf.float32, None, 'td_error')  
        
        l1 = tf.layers.dense(self.s, 30, activation=tf.nn.relu)
        mu = tf.layers.dense(l1, n_actions, activation=tf.nn.tanh)
        sigma = tf.layers.dense(l1, n_actions, activation=tf.nn.softplus)
        global_step = tf.Variable(0, trainable=False)  
        
        self.mu, self.sigma = tf.squeeze(mu*2), tf.squeeze(sigma+0.1)
        self.normal_dist = tf.distributions.Normal(self.mu, self.sigma)
        
        # 最大熵
        self.log_prob = self.normal_dist.log_prob(self.a)
        self.exp_v = self.log_prob * self.td_error
        self.entropy = self.normal_dist.entropy()  
        self.loss = -(self.exp_v + 0.01*self.entropy)  
        self.train_op = tf.train.AdamOptimizer(lr).minimize(self.loss, global_step=global_step)
        
    def learn(self, s, a, td):
        self.sess.run(self.train_op, {self.s: s, self.a: a, self.td_error: td})
        
    def choose_action(self, s):
        s = s[np.newaxis, :]
        return self.sess.run(self.normal_dist.sample(), {self.s: s})[0]

# 定义Critic网络
class Critic(object):
    def __init__(self, sess, n_features, lr=0.002):
        self.sess = sess
        self.s = tf.placeholder(tf.float32, [None, n_features], 'state')
        self.v_ = tf.placeholder(tf.float32, [None, 1], 'v_next')
        self.r = tf.placeholder(tf.float32, None, 'r')
        
        l1 = tf.layers.dense(self.s, 30, activation=tf.nn.relu)
        self.v = tf.layers.dense(l1, 1, activation=None)
        td = tf.reduce_mean(self.r + GAMMA * self.v_ - self.v)
        self.td_error = tf.reduce_mean(tf.square(td))  
        self.train_op = tf.train.AdamOptimizer(lr).minimize(self.td_error)
        
    def learn(self, s, r, s_):
        v_ = self.sess.run(self.v, {self.s: s_})
        td_error, _ = self.sess.run([self.td_error, self.train_op],
                                          {self.s: s, self.v_: v_, self.r: r})
        return td_error

# 经验回放
class Memory(object):
    def __init__(self, capacity, dims):
        self.capacity = capacity
        self.data = np.zeros((capacity, dims))
        self.pointer = 0
        
    def store_transition(self, s, a, r, s_):
        transition = np.hstack((s, a, r, s_))
        index = self.pointer % self.capacity  
        self.data[index, :] = transition
        self.pointer += 1
        
    def sample(self, n):
        assert self.pointer >= self.capacity, '记忆库数据不够多'
        indices = np.random.choice(self.capacity, size=n)
        return self.data[indices, :]

# 主函数
if __name__ == "__main__":
    env = gym.make('Pendulum-v0')
    env.seed(1)
    np.random.seed(1)
    tf.set_random_seed(1)
    
    sess = tf.Session()

    # 创建actor和critic网络
    actor = Actor(sess, env.observation_space.shape[0], env.action_space.shape[0], LR_A)
    critic = Critic(sess, env.observation_space.shape[0], LR_C)
    sess.run(tf.global_variables_initializer())

    # 创建记忆库
    memory = Memory(MEMORY_CAPACITY, dims=env.observation_space.shape[0] * 2 + env.action_space.shape[0] + 1)

    # 记录训练过程中的奖励和误差
    reward_list = []
    td_error_list = []

    for i in range(MAX_EPISODES):
        s = env.reset()
        ep_reward = 0
        ep_td_error = 0

        for j in range(MAX_EP_STEPS):
            env.render()

            # actor选择动作
            a = actor.choose_action(s)

            # 执行动作，得到下一个状态和奖励
            s_, r, done, info = env.step(a)

            # 存储经验
            memory.store_transition(s, a, r/10, s_)

            # 如果记忆库数据够多，开始训练
            if memory.pointer > MEMORY_CAPACITY:
                # 抽取记忆库数据
                batch_memory = memory.sample(BATCH_SIZE)

                # 更新critic网络
                td_error = critic.learn(batch_memory[:, :env.observation_space.shape[0]],
                                        batch_memory[:, -env.observation_space.shape[0]:],
                                        batch_memory[:, -env.observation_space.shape[0]-1:-1])
                ep_td_error += td_error

                # 更新actor网络
                actor.learn(batch_memory[:, :env.observation_space.shape[0]],
                            batch_memory[:, env.observation_space.shape[0]:env.observation_space.shape[0] + env.action_space.shape[0]],
                            td_error)

            # 状态更新
            s = s_
            ep_reward += r

            # 如果一次训练结束，输出信息
            if j == MAX_EP_STEPS-1:
                print('Episode:', i, ' Reward: %i' % int(ep_reward), ' TD Error: %.4f' % float(ep_td_error/BATCH_SIZE))
                reward_list.append(ep_reward)
                td_error_list.append(ep_td_error/BATCH_SIZE)
                break

    # 关闭环境
    env.close()
    sess.close()

这段代码实现了一个简单的Pendulum-v0任务的最大熵SAC强化学习算法，其中Actor和Critic网络均采用了单层神经网络，可以根据需要进行修改。在训练过程中，会输出每一次训练的奖励和TD误差。