强化学习 - Actor-Critic（演员-评论家算法）_深度强化学习 演员-评论家算法

什么是机器学习

Actor-Critic（演员-评论家算法）是一种结合了策略梯度方法（Actor）和值函数方法（Critic）的强化学习算法。演员（Actor）学习策略，决定在给定状态下应该采取什么动作；评论家（Critic）学习值函数，评估当前状态的价值。

以下是一个使用 Python 和 TensorFlow/Keras 实现简单的Actor-Critic算法的示例。在这个例子中，我们将使用 OpenAI Gym 的 CartPole 环境。

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Dense
from tensorflow.keras.optimizers import Adam
import gym

# 定义Actor-Critic Agent
class ActorCriticAgent:
    def __init__(self, state_size, action_size):
        self.state_size = state_size
        self.action_size = action_size
        self.gamma = 0.99  # 折扣因子
        self.learning_rate = 0.01

        # 构建演员（Actor）网络
        self.actor = self.build_actor()
        
        # 构建评论家（Critic）网络
        self.critic = self.build_critic()

    def build_actor(self):
        state_input = Input(shape=(self.state_size,))
        dense1 = Dense(24, activation='relu')(state_input)
        dense2 = Dense(24, activation='relu')(dense1)
        output = Dense(self.action_size, activation='softmax')(dense2)
        model = Model(inputs=state_input, outputs=output)
        model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=self.learning_rate))
        return model

    def build_critic(self):
        state_input = Input(shape=(self.state_size,))
        dense1 = Dense(24, activation='relu')(state_input)
        dense2 = Dense(24, activation='relu')(dense1)
        output = Dense(1, activation='linear')(dense2)
        model = Model(inputs=state_input, outputs=output)
        model.compile(loss='mean_squared_error', optimizer=Adam(lr=self.learning_rate))
        return model

    def get_action(self, state):
        state = np.reshape(state, [1, self.state_size])
        action_prob = self.actor.predict(state)[0]
        action = np.random.choice(self.action_size, p=action_prob)
        return action

    def train(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])

        # 计算TD误差（Temporal Difference error）
        target = reward + self.gamma * self.critic.predict(next_state)[0][0] * (1 - done)
        td_error = target - self.critic.predict(state)[0][0]

        # 训练演员网络
        action_one_hot = tf.keras.utils.to_categorical(action, self.action_size)
        actor_grads = -1 * np.log(action_one_hot) * td_error
        self.actor.train_on_batch(state, actor_grads)

        # 训练评论家网络
        critic_target = np.reshape(target, (1, 1))
        self.critic.train_on_batch(state, critic_target)

# 初始化环境和Agent
env = gym.make('CartPole-v1')
state_size = env.observation_space.shape[0]
action_size = env.action_space.n
agent = ActorCriticAgent(state_size, action_size)

# 训练Actor-Critic Agent
num_episodes = 1000
for episode in range(num_episodes):
    state = env.reset()
    total_reward = 0
    for time in range(500):  # 限制每个episode的步数，防止无限循环
        # env.render()  # 如果想可视化训练过程，可以取消注释此行
        action = agent.get_action(state)
        next_state, reward, done, _ = env.step(action)
        total_reward += reward
        agent.train(state, action, reward, next_state, done)
        state = next_state
        if done:
            print("Episode: {}, Total Reward: {}".format(episode + 1, total_reward))
            break

# 关闭环境
env.close()
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在这个例子中，我们定义了一个简单的Actor-Critic Agent，包括演员（Actor）和评论家（Critic）两个神经网络。Agent在每个时间步中通过演员网络选择动作，然后通过评论家网络进行训练。演员网络输出动作的概率，评论家网络输出当前状态的值。

请注意，Actor-Critic算法的实现可能因问题的复杂性而有所不同，可能需要更多的技术和调整，如基线（baseline）、归一化奖励、使用更复杂的神经网络结构等。