深度强化学习算法与应用研究现状综述

An overview on algorithms and applications of deep reinforcement learning

深度强化学习主要被用来处理感知-决策问题,已经成为人工智能领域重要的研究分支。概述了基于值函数和策略梯度的两类深度强化学习算法,详细阐述了深度Q网络、深度策略梯度及相关改进算法的原理,并综述了深度强化学习在视频游戏、导航、多智能体协作以及推荐系统等领域的应用研究进展。最后,对深度强化学习的算法和应用进行展望,针对一些未来的研究方向和研究热点给出了建议。

Deep reinforcement learning(DRL)is mainly applied to solve the perception-decision problem,and has become an important research branch in the field of artificial intelligence.Two kinds of DRL algorithms based on value function and policy gradient were summarized,including deep Q network,policy gradient as well as related developed algorithms.In addition,the applications of DRL in video games,navigation,multi-agent cooperation and recommendation field were intensively reviewed.Finally,a prospect for the future research of DRL was made,and some research suggestions were given.