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
10.5626/JOK.2025.52.5.374


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  Abstract

The law of diminishing marginal utility is an economic theory stating that as additional units of a good are consumed, the utility gained from each additional unit is decreased. We incorporated the law of diminishing marginal utility into multi-agent reinforcement learning for resource allocation, demonstrating that optimal distribution could emerge without direct communication among agents. This approach aligns with market principles, where individual self-Ainterested actions can lead to maximization of total utility. Experimental results in a grid-world environment showed that when two agents competed for two resources, applying the law of diminishing marginal utility led to a more equitable and Pareto-optimal allocation of resources.


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  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.


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