Explainable Reinforcement Learning: Basic Problems Exploration and Method Survey
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    Reinforcement learning is a technique that discovers optimal behavior strategies in a trial-and-error way, and it has become a general method for solving environmental interaction problems. However, as a machine learning method, reinforcement learning faces a common problem in machine learning, or in other words, it is unexplainable. The unexplainable problem limits the application of reinforcement learning in safety-sensitive fields, e.g., medical treatment and transportation, and it leads to a lack of universally applicable solutions in environmental simulation and task generalization. In order to address the problem, extensive research on explainable reinforcement learning (XRL) has emerged. However, academic members still have an inconsistent understanding of XRL. Therefore, this study explores the basic problems of XRL and reviews existing works. To begin with, the study discusses the parent problem, i.e., explainable artificial intelligence, and summarizes its existing definitions. Next, it constructs a theoretical system of interpretability to describe the common problems of XRL and explainable artificial intelligence. To be specific, it distinguishes between intelligent algorithms and mechanical algorithms, defines interpretability, discusses factors that affect interpretability, and classifies the intuitiveness of interpretability. Then, based on the characteristics of reinforcement learning, the study defines three unique problems of XRL, i.e., environmental interpretation, task interpretation, and strategy interpretation. After that, the latest research on XRL is reviewed, and the existing methods were systematically classified. Finally, the future research directions of XRL are put forward.

    Reference
    Related
    Cited by
Get Citation

刘潇,刘书洋,庄韫恺,高阳.强化学习可解释性基础问题探索和方法综述.软件学报,2023,34(5):2300-2316

Copy
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:February 23,2021
  • Revised:July 16,2021
  • Adopted:
  • Online: October 20,2021
  • Published: May 06,2023
You are the firstVisitors
Copyright: Institute of Software, Chinese Academy of Sciences Beijing ICP No. 05046678-4
Address:4# South Fourth Street, Zhong Guan Cun, Beijing 100190,Postal Code:100190
Phone:010-62562563 Fax:010-62562533 Email:jos@iscas.ac.cn
Technical Support:Beijing Qinyun Technology Development Co., Ltd.

Beijing Public Network Security No. 11040202500063