# MFGLib: A Library for Mean-Field Games

arXiv: https://arxiv.org/abs/2304.08630 · HTML: https://mlsanity.com/paper/2304.08630/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2023.

## Built on by (most cited first, 6 of 6)

- [Learning Decentralized Partially Observable Mean Field Control for Artificial Collective Behavior](https://mlsanity.com/paper/2307.06175/) (2023)
- [MF-OML: Online Mean-Field Reinforcement Learning with Occupation Measures for Large Population Games](https://mlsanity.com/paper/2405.00282/) (2024)
- [Exploiting Approximate Symmetry for Efficient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2408.15173/) (2024)
- [MESOB: Balancing Equilibria & Social Optimality](https://mlsanity.com/paper/2307.07911/) (2023)
- [Learning in Stackelberg Mean Field Games: A Non-Asymptotic Analysis](https://mlsanity.com/paper/2509.15392/) (2025)
- [Recurrent Structural Policy Gradient for Partially Observable Mean Field Games](https://mlsanity.com/paper/2602.20141/) (2026)

## Abstract (arXiv)

Mean-field games (MFGs) are limiting models to approximate N-player games, with a number of applications. Despite the ever-growing numerical literature on computation of MFGs, there is no library that allows researchers and practitioners to easily create and solve their own MFG problems. The purpose of this document is to introduce MFGLib, an open-source Python library for solving general MFGs with a user-friendly and customizable interface. It serves as a handy tool for creating and analyzing generic MFG environments, along with embedded auto-tuners for all implemented algorithms. The package is distributed under the MIT license and the source code and documentation can be found at https://github.com/radar-research-lab/MFGLib/.