We study the robustness of an agent decision-making model in finite-population games, with a particular focus on the Kullback-Leibler Divergence Regularized Learning (KLD-RL) model. Specifically, we examine how the model's parameters influence the impact of various sources of noise and modeling inaccuracies -- factors commonly encountered in engineering applications of population games -- on agents' decision-making. Our analysis provides insights into how these parameters can be effectively tuned to mitigate such effects. Theoretical results are supported by numerical examples and simulation studies that validate the analysis and illustrate practical strategies for parameter selection.
@article{arxiv.2505.06200,
title = {Robust Multi-Agent Decision-Making in Finite-Population Games},
author = {Shinkyu Park and Lucas C. D. Bezerra},
journal= {arXiv preprint arXiv:2505.06200},
year = {2025}
}