写一个基于tensorflow2.0的A3C强化学习算法python程序

好的，这是您的第一个问题。以下是基于Tensorflow 2.0的A3C强化学习算法的Python程序：

import tensorflow as tf
import numpy as np
import gym

# 设置超参数
learning_rate = 0.0001
gamma = 0.99
n_workers = 8  # 线程数
n_steps = 5  # n步奖励
batch_size = n_workers * n_steps
n_episodes = 10000

# 定义Actor-Critic模型
class ActorCritic(tf.keras.Model):
    def __init__(self):
        super(ActorCritic, self).__init__()

        self.dense1 = tf.keras.layers.Dense(128, activation='relu')
        self.policy_logits = tf.keras.layers.Dense(2)
        self.dense2 = tf.keras.layers.Dense(128, activation='relu')
        self.values = tf.keras.layers.Dense(1)

    def call(self, inputs):
        x = self.dense1(inputs)
        logits = self.policy_logits(x)
        v_preds = self.values(self.dense2(x))

        return logits, v_preds

env = gym.make('CartPole-v0')
model = ActorCritic()

optimizer = tf.keras.optimizers.Adam(learning_rate)

# 用于计算n步奖励的函数
def get_n_step_rewards(rewards, v_preds, done):
    n = len(rewards)
    rewards = rewards + [0.0] * (n_steps - (n % n_steps))

    if not done:
        _, last_v_pred = model(tf.constant([env.reset()], dtype=tf.float32)) 
        v_preds = v_preds + [last_v_pred.numpy()[0]]

    n_step_rs = []

    for i in range(0, n, n_steps):
        V = sum(n_steps_rs * gamma**np.arange(len(n_steps_rs)))  
        n_step_rs += [V] * n_steps_rs

    return np.array(n_step_rs)

# 开始训练
for episode in range(n_episodes):
    states = np.zeros((batch_size, 4))
    actions = np.zeros((batch_size,))
    rewards = np.zeros((batch_size,))
    v_preds = np.zeros((batch_size,))
    next_states = np.zeros((batch_size, 4))
    dones = np.zeros((batch_size,))

    s = env.reset()
    for i in range(batch_size):
        states[i] = s

        logits, v_pred = model(tf.constant([s], dtype=tf.float32))

        a = tf.squeeze(tf.random.categorical(logits, 1)).numpy()[0]

        s_, r, done, _ = env.step(a)

        actions[i] = a
        rewards[i] = r
        v_preds[i] = v_pred.numpy()[0]
        next_states[i] = s_
        dones[i] = done

        if done:
            s = env.reset()
        else:
            s = s_

    n_step_rs = get_n_step_rewards(rewards, v_preds, done)

    with tf.GradientTape() as tape:
        logits, v_preds = model(tf.constant(states, dtype=tf.float32))
        log_probs = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=actions)

        advantage = tf.constant(n_step_rs, dtype=tf.float32) - v_preds
        critic_loss = tf.reduce_mean(tf.square(advantage))

        actor_loss = tf.reduce_mean(log_probs * tf.stop_gradient(advantage))
        entropy_loss = tf.reduce_mean(tf.nn.softmax(logits) * tf.nn.log_softmax(logits))

        total_loss = actor_loss + 0.5 * critic_loss - 0.01 * entropy_loss

    grads = tape.gradient(total_loss, model.trainable_variables)
    optimizer.apply_gradients(zip(grads, model.trainable_variables))

    print('Episode: {} \t\t Avg Reward: {}'.format(episode, np.mean(rewards)))

希望这个Python程序可以对您有所帮助。如果您有其他问题，请随时问我。