Deep reinforcement learning:a survey

作     者：Hao-nan WANG Ning LIU Yi-yun ZHANG Da-wei FENG Feng HUANG Dong-sheng LI Yi-ming ZHANG Hao-nan WANG;Ning LIU;Yi-yun ZHANG;Da-wei FENG;Feng HUANG;Dong-sheng LI;Yi-ming ZHANG

作者机构：Science and Technology on Parallel and Distributed Processing LaboratoryNational University of Defense TechnologyChangsha 41OOOOChina 

出 版 物：《Frontiers of Information Technology & Electronic Engineering》 (信息与电子工程前沿（英文版）)

年 卷 期：2020年第21卷第12期

页      面：1726-1744页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project supported by the National Natural Science Foundation of China(Nos.61772541 61872376 and 61932001) 

主　　题：Reinforcement learning Deep reinforcement learning Reinforcement learning applications 

摘      要：Deep reinforcement learning(RL)has become one of the most popular topics in artificial intelligence *** has been widely used in various fields,such as end-to-end control,robotic control,recommendation systems,and natural language dialogue *** this survey,we systematically categorize the deep RL algorithms and applications,and provide a detailed review over existing deep RL algorithms by dividing them into modelbased methods,model-free methods,and advanced RL *** thoroughly analyze the advances including exploration,inverse RL,and transfer ***,we outline the current representative applications,and analyze four open problems for future research.