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[2602.12517] Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games
Summary
The paper presents Bench-MFG, a benchmark suite designed to standardize evaluations in learning for stationary Mean Field Games, addressing fragmentation in current methodologies.
Why It Matters
As Mean Field Games (MFGs) intersect with Reinforcement Learning (RL), a lack of standardized evaluation protocols hampers the assessment of new algorithms. Bench-MFG aims to fill this gap, providing a structured approach to evaluate and compare various learning methods, which is crucial for advancing research in multi-agent systems.
Key Takeaways
- Bench-MFG introduces a comprehensive benchmark suite for evaluating algorithms in stationary Mean Field Games.
- The suite includes a taxonomy of problem classes to facilitate diverse testing environments.
- MF-Garnets is proposed for generating random MFG instances, enhancing statistical testing rigor.
- The paper benchmarks various learning algorithms, including a novel black-box approach for exploitability minimization.
- Guidelines for standardizing future experimental comparisons are provided based on empirical results.
Computer Science > Machine Learning arXiv:2602.12517 (cs) [Submitted on 13 Feb 2026] Title:Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games Authors:Lorenzo Magnino, Jiacheng Shen, Matthieu Geist, Olivier Pietquin, Mathieu Laurière View a PDF of the paper titled Bench-MFG: A Benchmark Suite for Learning in Stationary Mean Field Games, by Lorenzo Magnino and 4 other authors View PDF HTML (experimental) Abstract:The intersection of Mean Field Games (MFGs) and Reinforcement Learning (RL) 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 (Bench-MFG), focusing on the discrete-time, discrete-space, stationary setting for the sake of clarity. 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 propose 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 black-box approach (MF-PSO) for exploitab...
