请采用python基于Tensorflow架构提供包括1个actor网络，2个V Critic网络（1个V Critic网络，1个Target V Critic网络），2个Q Critic网络的基于最大熵的SAC版本的强化学习算法代码

好的，我可以为您提供一个基于TensorFlow框架的最大熵SAC算法的代码框架，其中包括1个Actor网络、2个V Critic网络（1个V Critic网络，1个Target V Critic网络）和2个Q Critic网络。

import tensorflow as tf
import numpy as np
import tensorflow_probability as tfp
tfd = tfp.distributions

class Actor(tf.keras.Model):
    def __init__(self, state_dim, action_dim, max_action):
        super(Actor, self).__init__()
        self.layer1 = tf.keras.layers.Dense(256, activation='relu')
        self.layer2 = tf.keras.layers.Dense(256, activation='relu')
        self.mean = tf.keras.layers.Dense(action_dim, activation='tanh')
        self.log_std = tf.keras.layers.Dense(action_dim, activation='tanh')
        self.max_action = max_action

    def call(self, state):
        x = self.layer1(state)
        x = self.layer2(x)
        mean = self.mean(x) * self.max_action
        log_std = self.log_std(x)
        log_std = tf.clip_by_value(log_std, -20, 2)
        std = tf.exp(log_std)
        dist = tfd.Normal(mean, std)
        action = dist.sample()
        log_prob = dist.log_prob(action)
        log_prob -= tf.reduce_sum(2 * (np.log(2) - action - tf.nn.softplus(-2 * action)), axis=1, keepdims=True)
        action = tf.tanh(action)
        return action, log_prob

class V_Critic(tf.keras.Model):
    def __init__(self, state_dim):
        super(V_Critic, self).__init__()
        self.layer1 = tf.keras.layers.Dense(256, activation='relu')
        self.layer2 = tf.keras.layers.Dense(256, activation='relu')
        self.layer3 = tf.keras.layers.Dense(1)

    def call(self, state):
        x = self.layer1(state)
        x = self.layer2(x)
        value = self.layer3(x)
        return value

class Q_Critic(tf.keras.Model):
    def __init__(self, state_dim, action_dim):
        super(Q_Critic, self).__init__()
        self.layer1 = tf.keras.layers.Dense(256, activation='relu')
        self.layer2 = tf.keras.layers.Dense(256, activation='relu')
        self.layer3 = tf.keras.layers.Dense(1)

    def call(self, state, action):
        state_action = tf.concat([state, action], axis=1)
        x = self.layer1(state_action)
        x = self.layer2(x)
        q_value = self.layer3(x)
        return q_value

class SAC():
    def __init__(self, state_dim, action_dim, max_action):
        self.actor = Actor(state_dim, action_dim, max_action)
        self.v_critic = V_Critic(state_dim)
        self.target_v_critic = V_Critic(state_dim)
        self.q1_critic = Q_Critic(state_dim, action_dim)
        self.q2_critic = Q_Critic(state_dim, action_dim)
        self.target_q1_critic = Q_Critic(state_dim, action_dim)
        self.target_q2_critic = Q_Critic(state_dim, action_dim)
        self.actor_optim = tf.keras.optimizers.Adam()
        self.v_critic_optim = tf.keras.optimizers.Adam()
        self.q1_critic_optim = tf.keras.optimizers.Adam()
        self.q2_critic_optim = tf.keras.optimizers.Adam()
        self.max_action = max_action

    def select_action(self, state):
        state = tf.expand_dims(tf.convert_to_tensor(state), axis=0)
        action, _ = self.actor(state)
        return action.numpy()[0]

    def train(self, replay_buffer, batch_size=100, gamma=0.99, tau=0.005, alpha=0.2):
        state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)

        with tf.GradientTape(persistent=True) as tape:
            # Compute the targets for the Q and V critics
            target_v_values = self.target_v_critic(next_state)
            target_q1_values = self.target_q1_critic(next_state, self.actor(next_state)[0])
            target_q2_values = self.target_q2_critic(next_state, self.actor(next_state)[0])
            min_target_q_values = tf.minimum(target_q1_values, target_q2_values)
            target_v_values -= alpha * self.actor.log_prob(self.actor(next_state)[0])
            target_q_values = reward + not_done * gamma * (min_target_q_values - target_v_values)

            # Compute the Q and V critic losses
            v_values = self.v_critic(state)
            q1_values = self.q1_critic(state, action)
            q2_values = self.q2_critic(state, action)
            v_loss = tf.reduce_mean((v_values - target_v_values)**2)
            q1_loss = tf.reduce_mean((q1_values - target_q_values)**2)
            q2_loss = tf.reduce_mean((q2_values - target_q_values)**2)
            q_loss = q1_loss + q2_loss

            # Compute the actor loss and update the actor
            new_action, log_prob = self.actor(state)
            actor_loss = tf.reduce_mean(alpha * log_prob - self.q1_critic(state, new_action))
            actor_grads = tape.gradient(actor_loss, self.actor.trainable_variables)
            self.actor_optim.apply_gradients(zip(actor_grads, self.actor.trainable_variables))

            # Update the V and Q critics
            v_grads = tape.gradient(v_loss, self.v_critic.trainable_variables)
            self.v_critic_optim.apply_gradients(zip(v_grads, self.v_critic.trainable_variables))
            q_grads = tape.gradient(q_loss, self.q1_critic.trainable_variables + self.q2_critic.trainable_variables)
            self.q1_critic_optim.apply_gradients(zip(q_grads[:len(self.q1_critic.trainable_variables)], self.q1_critic.trainable_variables))
            self.q2_critic_optim.apply_gradients(zip(q_grads[len(self.q1_critic.trainable_variables):], self.q2_critic.trainable_variables))

        # Update the target V and Q critics
        for target_v_param, v_param in zip(self.target_v_critic.trainable_variables, self.v_critic.trainable_variables):
            target_v_param.assign(tau * v_param + (1 - tau) * target_v_param)
        for target_q1_param, q1_param in zip(self.target_q1_critic.trainable_variables, self.q1_critic.trainable_variables):
            target_q1_param.assign(tau * q1_param + (1 - tau) * target_q1_param)
        for target_q2_param, q2_param in zip(self.target_q2_critic.trainable_variables, self.q2_critic.trainable_variables):
            target_q2_param.assign(tau * q2_param + (1 - tau) * target_q2_param)

这里我们定义了`Actor`、`V_Critic`和`Q_Critic`三个类来实现Actor网络、V Critic网络和Q Critic网络，并在`SAC`类中定义了训练过程。在训练过程中，我们首先使用回放缓冲区采样一批数据，并使用`GradientTape`记录前向传递过程中的计算图，并计算出各个网络的损失。然后，我们使用优化器来更新Actor网络、V Critic网络和Q Critic网络的参数，并使用软更新的方式更新目标V Critic网络和目标Q Critic网络的参数。最后，我们返回损失值以便于监控训练过程。