actor—critic算法Python

Actor-Critic算法是一种强化学习算法，在推广学习和计划学习的基础上引入了一个观察策略和一个评价策略。 其中，Actor策略用于选择下一个动作，Critic策略用于评估动作的价值。

实现Actor-Critic算法的代码可以使用Python语言实现，您可以使用强化学习库如TensorFlow，PyTorch或Keras等进行实现。 以下是一个使用TensorFlow的示例代码：

import tensorflow as tf
import numpy as np

class ActorCritic:
  def __init__(self, state_size, action_size, learning_rate):
    self.state_size = state_size
    self.action_size = action_size
    self.learning_rate = learning_rate

    self.build_model()
    self.sess = tf.Session()
    self.sess.run(tf.global_variables_initializer())

  def build_model(self):
    self.states = tf.placeholder(tf.float32, [None, self.state_size], name="states")
    self.actions = tf.placeholder(tf.int32, [None, self.action_size], name="actions")
    self.discounted_rewards = tf.placeholder(tf.float32, [None,], name="discounted_rewards")

    self.actor_fc1 = tf.layers.dense(self.states, units=32, activation=tf.nn.relu)
    self.actor_fc2 = tf.layers.dense(self.actor_fc1, units=32, activation=tf.nn.relu)
    self.actor_logits = tf.layers.dense(self.actor_fc2, units=self.action_size, activation=None)
    self.actor_probs = tf.nn.softmax(self.actor_logits)

    self.critic_fc1 = tf.layers.dense(self.states, units=32, activation=tf.nn.relu)
    self.critic_fc2 = tf.layers.dense(self.critic_fc1, units=32, activation=tf.nn.relu)
    self.critic_logits = tf.layers.dense(self.critic_fc2, units=1, activation=None)
    self.critic_value = tf.squeeze(self.critic_logits)

    self.actor_loss = -tf.reduce_mean