Knowledge-based deep reinforcement learning: a review

被引：8

作者：

Li, Chenxi ^{[1
]}

Cao, Lei ^{[1
]}

Zhang, Yongliang ^{[1
]}

Chen, Xiliang ^{[1
]}

Zhou, Yuhuan ^{[1
]}

Duan, Liwen ^{[2
]}

机构：

[1] Institute of Command Information System, PLA University of Science and Technology, Nanjing,210007, China

[2] College of Mechanical Engineering, Zhejiang University, Hangzhou,310027, China

来源：

Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics | 2017年 / 39卷 / 11期

关键词：

Inverse problems - Deep learning - Knowledge based systems;

D O I：

10.3969/j.issn.1001-506X.2017.11.30

中图分类号：

学科分类号：

摘要：

As an important method to solve sequential decision problems, reinforcement learning adopts a mechanism of trial and error to interact with the environment, in order to learn the policy of the task. Know-ledge, as a kind of structured information, which contains the elements of experience, values, cognitive rules and expert opinions, can be effectively used to improve the learning efficiency of reinforcement learning. This paper takes the basic theory of reinforcement learning as a starting point, and systematically summarizes the deep reinforcement learning and knowledge-based reinforcement learning. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.

引用

页码：2603 / 2613