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

以下是基于TensorFlow实现的SAC算法代码，其中使用了最大熵策略。

import tensorflow as tf
import tensorflow_probability as tfp
import numpy as np

class SAC:
    def __init__(self, state_dim, action_dim, hidden_dim, gamma=0.99, alpha=0.2, lr=3e-4):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim
        self.gamma = gamma
        self.alpha = alpha
        self.lr = lr

        self.actor = self.build_actor()
        self.critic = self.build_critic()
        self.target_critic = self.build_critic()

        self.actor_optimizer = tf.keras.optimizers.Adam(self.lr)
        self.critic_optimizer = tf.keras.optimizers.Adam(self.lr)

    def build_actor(self):
        inputs = tf.keras.layers.Input(shape=(self.state_dim,))
        x = tf.keras.layers.Dense(self.hidden_dim, activation='relu')(inputs)
        x = tf.keras.layers.Dense(self.hidden_dim, activation='relu')(x)
        mu = tf.keras.layers.Dense(self.action_dim, activation='tanh')(x)
        sigma = tf.keras.layers.Dense(self.action_dim, activation='softplus')(x)

        return tf.keras.Model(inputs=inputs, outputs=[mu, sigma])

    def build_critic(self):
        inputs = tf.keras.layers.Input(shape=(self.state_dim+self.action_dim,))
        x = tf.keras.layers.Dense(self.hidden_dim, activation='relu')(inputs)
        x = tf.keras.layers.Dense(self.hidden_dim, activation='relu')(x)
        q = tf.keras.layers.Dense(1)(x)

        return tf.keras.Model(inputs=inputs, outputs=q)

    def get_action(self, state):
        state = np.reshape(state, [1, self.state_dim])
        mu, sigma = self.actor.predict(state)
        dist = tfp.distributions.Normal(mu, sigma)
        action = dist.sample()
        return np.squeeze(action.numpy(), axis=0)

    def train(self, batch):
        state_batch = np.array([b[0] for b in batch])
        action_batch = np.array([b[1] for b in batch])
        reward_batch = np.array([b[2] for b in batch])
        next_state_batch = np.array([b[3] for b in batch])
        done_batch = np.array([b[4] for b in batch])

        # Update critic
        with tf.GradientTape() as tape:
            next_actions_mu, next_actions_sigma = self.actor(next_state_batch)
            next_actions_dist = tfp.distributions.Normal(next_actions_mu, next_actions_sigma)
            next_actions = next_actions_dist.sample()
            next_q_values = self.target_critic(tf.concat([next_state_batch, next_actions], axis=-1))
            target_q_values = reward_batch + self.gamma * (1-done_batch) * next_q_values
            target_q_values = tf.stop_gradient(target_q_values)
            q_values = self.critic(tf.concat([state_batch, action_batch], axis=-1))
            critic_loss = tf.reduce_mean(tf.square(q_values - target_q_values))

        critic_grads = tape.gradient(critic_loss, self.critic.trainable_variables)
        self.critic_optimizer.apply_gradients(zip(critic_grads, self.critic.trainable_variables))

        # Update actor
        with tf.GradientTape() as tape:
            actions_mu, actions_sigma = self.actor(state_batch)
            actions_dist = tfp.distributions.Normal(actions_mu, actions_sigma)
            actions = actions_dist.sample()
            q_values = self.critic(tf.concat([state_batch, actions], axis=-1))
            entropy = tf.reduce_mean(actions_dist.entropy())

            actor_loss = -tf.reduce_mean(q_values + self.alpha * entropy)

        actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
        self.actor_optimizer.apply_gradients(zip(actor_grads, self.actor.trainable_variables))

        # Update target critic
        critic_weights = self.critic.get_weights()
        target_critic_weights = self.target_critic.get_weights()
        for i in range(len(critic_weights)):
            target_critic_weights[i] = self.gamma * target_critic_weights[i] + (1 - self.gamma) * critic_weights[i]
        self.target_critic.set_weights(target_critic_weights)

在这个实现中，使用了一个高斯分布来表示actor的策略，其中的均值通过神经网络输出。每个动作的方差通过另一个神经网络输出，并通过softplus函数保证方差为正。在训练过程中，通过使用最大熵策略来提高探索性，其中的熵系数通过超参数`alpha`来控制。同时，使用了一个critic网络来评估状态动作对的价值，其中的输入是状态和动作的拼接。使用延迟更新目标网络来减少更新过程中出现的震荡。在训练过程中，分别对actor和critic使用了不同的优化器进行更新。