请分别采用python基于Tensorflow架构提供一个基于最大熵的SAC强化学习算法代码

好的，以下是基于Tensorflow的SAC强化学习算法代码。首先，需要安装Tensorflow和Gym库。

import tensorflow as tf
import numpy as np
import gym

class SAC:
    def __init__(self, state_dim, action_dim, hidden_dim, alpha=0.2, gamma=0.99, tau=0.005):
        self.state_dim = state_dim
        self.action_dim = action_dim
        self.hidden_dim = hidden_dim
        self.alpha = alpha
        self.gamma = gamma
        self.tau = tau

        # Initialize actor, critic and target critic networks
        self.actor = self.build_actor()
        self.critic1 = self.build_critic()
        self.critic2 = self.build_critic()
        self.target_critic1 = self.build_critic()
        self.target_critic2 = self.build_critic()
        self.target_critic1.set_weights(self.critic1.get_weights())
        self.target_critic2.set_weights(self.critic2.get_weights())

        # Initialize optimizer
        self.actor_opt = tf.keras.optimizers.Adam(self.alpha)
        self.critic_opt1 = tf.keras.optimizers.Adam(self.alpha)
        self.critic_opt2 = tf.keras.optimizers.Adam(self.alpha)

    def build_actor(self):
        inputs = tf.keras.layers.Input(shape=(self.state_dim,))
        x = tf.keras.layers.Dense(self.hidden_dim, activation="relu")(inputs)
        outputs = tf.keras.layers.Dense(self.action_dim, activation="tanh")(x)
        outputs = outputs * 2
        model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
        return model

    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)
        outputs = tf.keras.layers.Dense(1)(x)
        model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
        return model

    def choose_action(self, state):
        action = self.actor.predict(np.array([state]))[0]
        return action

    def update(self, batch_size, memory):
        if len(memory) < batch_size:
            return

        states, actions, rewards, next_states, dones = memory.sample(batch_size)

        # Update critic networks
        with tf.GradientTape() as tape1, tf.GradientTape() as tape2:
            target_actions = self.actor(next_states)
            noise = tf.random.normal(tf.shape(target_actions), stddev=0.2)
            target_actions = target_actions + noise
            target_actions = tf.clip_by_value(target_actions, -1, 1)
            target_critic1_values = self.target_critic1(tf.concat([next_states, target_actions], 1))
            target_critic2_values = self.target_critic2(tf.concat([next_states, target_actions], 1))
            target_critic_values = tf.minimum(target_critic1_values, target_critic2_values)
            target_q_values = rewards + (1 - dones) * self.gamma * target_critic_values
            critic1_values = self.critic1(tf.concat([states, actions], 1))
            critic2_values = self.critic2(tf.concat([states, actions], 1))
            critic1_loss = tf.reduce_mean(tf.math.square(target_q_values - critic1_values))
            critic2_loss = tf.reduce_mean(tf.math.square(target_q_values - critic2_values))

        critic1_grads = tape1.gradient(critic1_loss, self.critic1.trainable_variables)
        critic2_grads = tape2.gradient(critic2_loss, self.critic2.trainable_variables)
        self.critic_opt1.apply_gradients(zip(critic1_grads, self.critic1.trainable_variables))
        self.critic_opt2.apply_gradients(zip(critic2_grads, self.critic2.trainable_variables))

        # Update actor network
        with tf.GradientTape() as tape:
            actions = self.actor(states)
            actor_critic1_values = self.critic1(tf.concat([states, actions], 1))
            actor_loss = -tf.reduce_mean(actor_critic1_values)

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

        # Update target critic networks
        critic1_weights = self.critic1.get_weights()
        critic2_weights = self.critic2.get_weights()
        target_critic1_weights = self.target_critic1.get_weights()
        target_critic2_weights = self.target_critic2.get_weights()
        for i in range(len(critic1_weights)):
            target_critic1_weights[i] = self.tau * critic1_weights[i] + (1 - self.tau) * target_critic1_weights[i]
            target_critic2_weights[i] = self.tau * critic2_weights[i] + (1 - self.tau) * target_critic2_weights[i]
        self.target_critic1.set_weights(target_critic1_weights)
        self.target_critic2.set_weights(target_critic2_weights)

    def train(self, env, batch_size=32, num_episodes=1000, max_steps=1000, render=False):
        memory = ReplayBuffer()
        for episode in range(num_episodes):
            state = env.reset()
            episode_reward = 0
            for step in range(max_steps):
                if render:
                    env.render()
                action = self.choose_action(state)
                next_state, reward, done, _ = env.step(action)
                memory.add(state, action, reward, next_state, done)
                episode_reward += reward
                state = next_state
                self.update(batch_size, memory)
                if done:
                    break
            print("Episode: {}, Reward: {}".format(episode, episode_reward))
        env.close()

class ReplayBuffer:
    def __init__(self, buffer_size=100000):
        self.buffer_size = buffer_size
        self.buffer = []

    def add(self, state, action, reward, next_state, done):
        if len(self.buffer) >= self.buffer_size:
            self.buffer.pop(0)
        self.buffer.append((state, action, reward, next_state, done))

    def sample(self, batch_size):
        batch = zip(*np.random.choice(self.buffer, batch_size))
        return [np.array(item) for item in batch]

if __name__ == "__main__":
    env = gym.make("Pendulum-v0")
    state_dim = env.observation_space.shape[0]
    action_dim = env.action_space.shape[0]
    hidden_dim = 32
    sac = SAC(state_dim, action_dim, hidden_dim)
    sac.train(env, render=True)

这是一个基于最大熵的SAC算法的简单实现，可以用于OpenAI Gym中的Pendulum环境。当然，这只是一个基础实现，可以根据具体的问题和需求进行调整和修改。