Robust Multi-Agent Decision-Making in Finite-Population Games

2025-05-09

AI Summary

This work investigates the robustness of multi-agent decision-making models in finite-population games, focusing on the KL-divergence-regularized learning (KLD-RL) framework. Addressing common disturbances such as model misspecification and environmental noise, we establish a theoretical linkage between model parameter sensitivity and interference resilience—first systematically revealing the robustness mechanism of KLD-RL in finite-population settings. Through rigorous robustness analysis, numerical simulations, and Monte Carlo validation, we formally prove that key regularization parameters effectively suppress the impact of noise and model errors. Based on this analysis, we propose a verifiable and practically implementable parameter tuning guideline. Experimental results demonstrate that the guideline significantly enhances decision stability and convergence reliability. The study thus provides both theoretical foundations and actionable guidance for engineering deployment of KLD-RL in real-world multi-agent systems.

Technology Category

• Multiagent Systems: Multiagent Learning
• Machine Learning: Adversarial Learning & Robustness
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 effects 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.

Problem

• Analyzing robustness of KLD-RL model in finite-population games
• Examining parameter impacts on noise and modeling inaccuracies
• Providing tuning strategies to mitigate decision-making disruptions

Innovation

• KLD-RL model for robust decision-making
• Parameter tuning mitigates noise effects
• Numerical examples validate theoretical analysis