Classification study of interpretable methods for reinforcement learning

Tang Lei
Niu Yuanyuan
Wang Ruijie
Xing Benbei
Wang Yiting
College of Information Engineering, Chang'an University, Xi'an 710018, China

Abstract

Reinforcement learning can achieve autonomous learning in dynamic and complex environments, which makes it widely used in fields such as law, medicine, and finance. However, reinforcement learning still faces many problems such as the unobservable global state space, strong dependence on the reward function, and uncertain causality, which results in its weak interpretability, seriously affecting its promotion in related fields. It will encounter limitations such as difficulty in judging whether the decision-making violates social legal and moral requirements, whether it is accurate and trustworthy, etc. In order to further understand the current status of interpretability research in reinforcement learning, this article discussed from the aspects of interpretable models, interpretable strategies, environment interaction and visualization, etc. Based on these, this article systematically discussed the research status of reinforcement learning interpretability, classified and explained its explainable methods, and finally proposed the future development direction of reinforcement learning interpretability.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0430
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 6
Section: Survey
Pages: 1601-1609
Serial Number: 1001-3695(2024)06-001-1601-09

Publish History

[2023-12-18] Accepted Paper
[2024-06-05] Printed Article

Cite This Article

唐蕾, 牛园园, 王瑞杰, 等. 强化学习的可解释方法分类研究 [J]. 计算机应用研究, 2024, 41 (6): 1601-1609. (Tang Lei, Niu Yuanyuan, Wang Ruijie, et al. Classification study of interpretable methods for reinforcement learning [J]. Application Research of Computers, 2024, 41 (6): 1601-1609. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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