写一个基于tensorflow2.0的A3C强化学习程序

好的，我理解了您的要求，以下是基于tensorflow2.0的A3C强化学习程序示例：

import tensorflow as tf
import gym
import numpy as np

# 定义网络模型
class ActorCritic(tf.keras.Model):
    def __init__(self, state_size, action_size, hidden_size=256):
        super(ActorCritic, self).__init__()
        self.actor_fc1 = tf.keras.layers.Dense(hidden_size, activation='relu')
        self.actor_fc2 = tf.keras.layers.Dense(action_size, activation='softmax')
        self.critic_fc1 = tf.keras.layers.Dense(hidden_size, activation='relu')
        self.critic_fc2 = tf.keras.layers.Dense(1, activation=None)

    def call(self, inputs):
        x = tf.cast(inputs, dtype=tf.float32)
        actor = self.actor_fc2(self.actor_fc1(x))
        critic = self.critic_fc2(self.critic_fc1(x))
        return actor, critic

# 定义A3C算法
class A3C():
    def __init__(self, state_size, action_size, global_net=None):
        self.state_size = state_size
        self.action_size = action_size
        self.global_net = global_net

        self.actor_critic = ActorCritic(self.state_size, self.action_size)
        self.actor_optimizer = tf.keras.optimizers.RMSprop(lr=0.0007)
        self.critic_optimizer = tf.keras.optimizers.RMSprop(lr=0.0007)

    def choose_action(self, state):
        state = np.reshape(state, [1, self.state_size])
        actor, _ = self.actor_critic(state)
        action_probs = tf.squeeze(actor).numpy()
        action = np.random.choice(range(self.action_size), p=action_probs)
        return action

    def learn(self, state, action, reward, next_state, done):
        state = np.reshape(state, [1, self.state_size])
        next_state = np.reshape(next_state, [1, self.state_size])
        with tf.GradientTape() as tape:
            actor, critic = self.actor_critic(state)
            next_actor, next_critic = self.actor_critic(next_state)
            td_error = reward + 0.99 * next_critic * (1 - int(done)) - critic

            actor_loss = -tf.math.log(tf.reduce_sum(tf.multiply(actor, tf.one_hot(action, self.action_size)), axis=1, keepdims=True)) * td_error
            critic_loss = tf.square(td_error)

            total_loss = actor_loss + critic_loss * 0.5

        grads = tape.gradient(total_loss, self.actor_critic.trainable_variables)
        self.actor_optimizer.apply_gradients(zip(grads, self.actor_critic.trainable_variables))

        grads = tape.gradient(critic_loss, self.actor_critic.trainable_variables)
        self.critic_optimizer.apply_gradients(zip(grads, self.actor_critic.trainable_variables))

# 定义训练过程
def train(global_net):
    env = gym.make('CartPole-v0')
    a3c = A3C(state_size=4, action_size=2, global_net=global_net)
    episod_reward_list = []

    for i in range(1000):
        state = env.reset()
        episode_reward = 0
        done = False
        while not done:
            action = a3c.choose_action(state)
            next_state, reward, done, _ = env.step(action)
            a3c.learn(state, action, reward, next_state, done)
            state = next_state
            episode_reward += reward

        episod_reward_list.append(episode_reward)

    return episod_reward_list

if __name__ == '__main__':
    global_net = ActorCritic(state_size=4, action_size=2)
    rewards = train(global_net)
    print(rewards)

以上代码定义了一个简单的A3C算法实现，使用CartPole-v0环境进行训练。您可以根据自己的数据、环境、网络结构等来进行修改和调整。希望能对您有所帮助！