深度强化学习理论及其应用综述

doi:10.16451/j.cnki.issn1003-6059.201901009

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Abstract Deep reinforcement learning(DRL) theory and applied research are deepening and it is now playing an important role in games, robot control, dialogue systems, automatic driving, etc. Meanwhile, due to shortcomings such as exploration-exploitation dilemma, sparse reward, sample collection hardness, poor model stability, DRL still has many problems for which researchers have proposed various solutions. New theories has further promoted the development of DRL, and opened up several new research fields of reinforcement learning, such as imitative learning, hierarchical reinforcement learning and meta-learning. This paper aims to explore and summarize future development of DRL, and a brief introduction of DRL theory, difficulties and applications is presented at the same time.

Received: 29 December 2018

ZTFLH:

TP 181

Fund:Supported by Key Program of National Natural Science Foundation of China(No.91748208), General Program of National Natural Science Foundation of China(No.61573268), Key Research and Development Program of Ministry of Science and Technology of China(No.2018ZX01028101), Key Research and Development

Corresponding Authors: LAN Xuguang, Ph.D., professor. His research interests include computer vision and machine learning.

About author:: WAN Lipeng, Ph.D. candidate. His research interests include deep reinforcement learning and coexisting-cooperative-cognitive robots.ZHANG Hanbo, Ph.D. candidate. His research interests include deep reinforcement learning and robot controlling.ZHENG Nanning, Ph.D., professor. His research interests include computer vision and pattern recognition.

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Cite this article:

WAN Lipeng,LAN Xuguang,ZHANG Hanbo等. A Review of Deep Reinforcement Learning Theory and Application[J]. , 2019, 32(1): 67-81.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201901009 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I1/67

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