Bench-MFG：平稳平均场博弈学习的基准套件
Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
Lorenzo Magnino, Jiacheng Shen, Matthieu Geist, Olivier Pietquin, Mathieu Laurière
AI 速读 基于摘要
作者提出首个面向离散时间、离散空间、平稳平均场博弈（MFG）的基准套件，包含问题分类、原型环境和随机实例生成方法，并对多种学习算法进行评测。
cs.LG · cs.AI · cs.MA · math.OC
查看原文摘要
The rise of Mean Field Games (MFGs) has fostered a growing family of algorithms designed to solve large-scale multi-agent systems. However, the field currently lacks a standardized evaluation protocol, forcing researchers to rely on bespoke, isolated, and often simplistic environments. This fragmentation makes it difficult to assess the robustness, generalization, and failure modes of emerging methods. To address this gap, we propose a comprehensive benchmark suite for MFGs, focusing on the discrete-time, discrete-space, stationary setting. We introduce a taxonomy of problem classes, ranging from no-interaction and monotone games to potential and dynamics-coupled games, and provide prototypical environments for each. Furthermore, we present MF-Garnets, a method for generating random MFG instances to facilitate rigorous statistical testing. We benchmark a variety of learning algorithms across these environments, including a novel population-based approach (MF-PSO). To our knowledge, this is the first benchmark for MFGs.