智能系统中的强化学习方法

Reinforcement Learning Methods in Intelligent Systems

作为人工智能领域的常用技术之一,强化学习、监督学习和非监督学习并列为三种机器学习范式.强化学习是指智能体与环境直接进行交互,通过最大化累计奖励学习最佳策略的过程.本文梳理了强化学习领域的发展历程,并介绍了经典的强化学习和基于深度网络的强化学习算法和模型,包括基于值函数和策略梯度的方法、演员-评论家算法、深度Q网络及其优化模型,最后对强化学习当前面临的研究挑战和未来的发展前景进行了讨论.

As one of the commonly used technologies in the field of artificial intelligence,reinforcement learning is listed alongside supervised learning and unsupervised learning as one of the three main machine learning paradigms.Reinforcement learning involves an agent interacting directly with the environment to learn the best strategy by maximizing cumulative rewards.This paper reviews the development of reinforcement learning,introduces classical algorithms and models in both reinforcement learning and deep reinforcement learning,including value function-based methods,policy gradient methods,actor-critic algorithms,deep Q-networks,and their optimization models.At the end of this paper,current research challenges and future prospects of reinforcement learning are discussed.