Reinforcement Learning with the Law of Diminishing Marginal Utility: Efficient and Equitable Resource Allocation in Multi-Agent Systems

Yunsu Lee, Byoung-Tak Zhang

http://doi.org/10.5626/JOK.2025.52.5.374

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.