Transparency of task dependencies of reinforcement learning in unmanned systems

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

Reinforcement learning has been applied widely, however there are yet no easy ways to explain both an agent’s behaviour or the complex unmanned systems that an agent is in. In
this paper, we design a framework and its corresponding algorithm to discover the hidden rules or relationships in the unmanned systems supported by policies obtained through Reinforcement Learning training. It is demonstrated experimentally that our approach is effective in building an explicit representation for revealing the hidden rules or constraints when an agent needs to accomplish some tasks in that unmanned systems.
Original languageEnglish
Title of host publicationProceedings of the 25th IEEE International Conference on Industrial Technology (ICIT'24)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages8
ISBN (Electronic)9798350340273
ISBN (Print)9798350340266
DOIs
Publication statusPublished - 5 Jun 2024
Event2024 ICIT – 25th IEEE International Conference on Industrial Technology - Bristol, United Kingdom
Duration: 25 Mar 202427 Mar 2024
https://iten.ieee-ies.org/past-events/2023/2024-icit-25th-ieee-international-conference-on-industrial-technology/

Publication series

NameIEEE International Conference on Industrial Technology
PublisherIEEE
ISSN (Print)2641-0184
ISSN (Electronic)2643-2978

Conference

Conference2024 ICIT – 25th IEEE International Conference on Industrial Technology
Abbreviated titleICIT 2024
Country/TerritoryUnited Kingdom
CityBristol
Period25/03/2427/03/24
Internet address

Bibliographical note

Publisher Copyright:
Copyright © 2024 by the Institute of Electrical and Electronics Engineers, Inc.

Fingerprint

Dive into the research topics of 'Transparency of task dependencies of reinforcement learning in unmanned systems'. Together they form a unique fingerprint.

Cite this