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

抱歉，我并不是一个可以实际编写代码的AI，我只能提供一些基本的代码框架和思路供参考。

首先，需要导入Tensorflow库和其他必要的库：

import tensorflow as tf
import numpy as np
import gym
from tensorflow.keras.layers import Input, Dense, concatenate
from tensorflow.keras.models import Model
from tensorflow.keras.optimizers import Adam
from tensorflow.keras import backend as K
from collections import deque

然后，我们需要定义一些常量和超参数：

GAMMA = 0.99
LR = 0.0001
BATCH_SIZE = 128
MEMORY_SIZE = 1000000
MEMORY_WARMUP_SIZE = 10000
TAU = 0.005
ENTROPY_WEIGHT = 0.2
TARGET_ENTROPY = -2.0

接下来，我们需要定义一个记忆库类来存储我们的经验：

class Memory():
    def __init__(self, memory_size):
        self.memory_size = memory_size
        self.memory = deque(maxlen=memory_size)

    def add(self, state, action, reward, next_state, done):
        self.memory.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        batch = np.random.permutation(len(self.memory))[:batch_size]
        states, actions, rewards, next_states, dones = [], [], [], [], []
        for i in batch:
            state, action, reward, next_state, done = self.memory[i]
            states.append(state)
            actions.append(action)
            rewards.append(reward)
            next_states.append(next_state)
            dones.append(done)
        return np.array(states), np.array(actions), np.array(rewards), np.array(next_states), np.array(dones)

    def __len__(self):
        return len(self.memory)

然后，我们需要定义一个神经网络类来构建我们的Actor和Critic网络：

class NeuralNetwork():
    def __init__(self, state_shape, action_shape, lr):
        self.state_shape = state_shape
        self.action_shape = action_shape
        self.lr = lr

    def build_actor(self):
        state_input = Input(self.state_shape)
        x = Dense(256, activation='relu')(state_input)
        x = Dense(256, activation='relu')(x)
        x = Dense(self.action_shape[0], activation='tanh')(x)
        action_output = Lambda(lambda x: x * 2)(x)
        model = Model(state_input, action_output)
        model.compile(loss='mse', optimizer=Adam(lr=self.lr))
        return model

    def build_critic(self):
        state_input = Input(self.state_shape)
        action_input = Input(self.action_shape)
        x = concatenate([state_input, action_input])
        x = Dense(256, activation='relu')(x)
        x = Dense(256, activation='relu')(x)
        value_output = Dense(1)(x)
        model = Model([state_input, action_input], value_output)
        model.compile(loss='mse', optimizer=Adam(lr=self.lr))
        return model

接下来，我们需要定义一个Soft Actor-Critic类来实现我们的算法：

class SAC():
    def __init__(self, env):
        self.env = env
        self.state_shape = env.observation_space.shape
        self.action_shape = env.action_space.shape
        self.action_bounds = env.action_space.high[0]
        self.memory = Memory(MEMORY_SIZE)
        self.actor = NeuralNetwork(self.state_shape, self.action_shape, LR).build_actor()
        self.v_critic = NeuralNetwork(self.state_shape, self.action_shape, LR).build_critic()
        self.target_v_critic = NeuralNetwork(self.state_shape, self.action_shape, LR).build_critic()
        self.q_critic_1 = NeuralNetwork(self.state_shape, self.action_shape, LR).build_critic()
        self.q_critic_2 = NeuralNetwork(self.state_shape, self.action_shape, LR).build_critic()

    def choose_action(self, state):
        state = state[np.newaxis, :]
        action = self.actor.predict(state)[0]
        noise = np.random.normal(0, self.action_bounds * 0.1, size=self.action_shape)
        action = np.clip(action + noise, -self.action_bounds, self.action_bounds)
        return action

    def train(self):
        if len(self.memory) < MEMORY_WARMUP_SIZE:
            return

        states, actions, rewards, next_states, dones = self.memory.sample(BATCH_SIZE)
        next_actions = self.actor.predict(next_states)
        target_v = self.target_v_critic.predict([next_states, next_actions])
        target_q = rewards + GAMMA * target_v * (1 - dones)
        self.q_critic_1.fit([states, actions], target_q, verbose=0)
        self.q_critic_2.fit([states, actions], target_q, verbose=0)

        actions = tf.convert_to_tensor(self.actor.predict(states))
        actions = tf.clip_by_value(actions, -self.action_bounds, self.action_bounds)
        q1 = self.q_critic_1([states, actions])
        q2 = self.q_critic_2([states, actions])
        v = self.v_critic.predict(states)
        v_target = tf.reduce_min([q1, q2], axis=0) - ENTROPY_WEIGHT * self.actor_entropy(states)
        v_loss = tf.reduce_mean(tf.square(v - v_target))
        self.v_critic.fit(states, v_target, verbose=0)

        with tf.GradientTape() as tape:
            new_actions = self.actor(states)
            entropy = self.actor_entropy(states)
            q1 = self.q_critic_1([states, new_actions])
            q2 = self.q_critic_2([states, new_actions])
            q = tf.reduce_min([q1, q2], axis=0)
            actor_loss = tf.reduce_mean(entropy - q)
        actor_grad = tape.gradient(actor_loss, self.actor.trainable_variables)
        self.actor.optimizer.apply_gradients(zip(actor_grad, self.actor.trainable_variables))

        for t, e in zip(self.target_v_critic.trainable_variables, self.v_critic.trainable_variables):
            t.assign(t * (1 - TAU) + e * TAU)

    def actor_entropy(self, states):
        actions = self.actor(states)
        log_probs = -0.5 * tf.reduce_sum(tf.square((actions - self.actor(states)) / self.action_bounds), axis=1)
        log_probs -= 0.5 * tf.cast(tf.size(actions), tf.float32) * np.log(2 * np.pi)
        return tf.reduce_mean(log_probs)

    def train_loop(self, episodes):
        for i in range(episodes):
            state = self.env.reset()
            total_reward = 0
            done = False
            while not done:
                action = self.choose_action(state)
                next_state, reward, done, _ = self.env.step(action)
                self.memory.add(state, action, reward, next_state, done)
                state = next_state
                total_reward += reward
                self.train()
            print("Episode: {}, Total Reward: {}".format(i, total_reward))

最后，我们可以创建一个SAC对象并开始训练：

env = gym.make('Pendulum-v0')
sac = SAC(env)
sac.train_loop(1000)

这样，就完成了基于Tensorflow架构的SAC算法代码的编写。需要注意的是，由于不同的环境和任务有不同的超参数和网络结构要求，以上代码仅供参考，具体实现需要根据实际情况进行调整。