Reinforcement Learning with the Law of Diminishing Marginal Utility: Efficient and Equitable Resource Allocation in Multi-Agent Systems
Vol. 52, No. 5, pp. 374-378, May 2025

Abstract
Statistics
Cumulative Counts from November, 2022
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.
Multiple requests among the same browser session are counted as one view. If you mouse over a chart, the values of data points will be shown.
|
Cite this article
[IEEE Style]
Y. Lee and B. Zhang, "Reinforcement Learning with the Law of Diminishing Marginal Utility: Efficient and Equitable Resource Allocation in Multi-Agent Systems," Journal of KIISE, JOK, vol. 52, no. 5, pp. 374-378, 2025. DOI: 10.5626/JOK.2025.52.5.374.
[ACM Style]
Yunsu Lee and Byoung-Tak Zhang. 2025. Reinforcement Learning with the Law of Diminishing Marginal Utility: Efficient and Equitable Resource Allocation in Multi-Agent Systems. Journal of KIISE, JOK, 52, 5, (2025), 374-378. DOI: 10.5626/JOK.2025.52.5.374.
[KCI Style]
이윤수, 장병탁, "한계효용 체감의 법칙을 적용한 강화학습: 다중 에이전트의 효율적이고 평등한 자원 분배," 한국정보과학회 논문지, 제52권, 제5호, 374~378쪽, 2025. DOI: 10.5626/JOK.2025.52.5.374.
[Endnote/Zotero/Mendeley (RIS)] Download
[BibTeX] Download
Search

Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr