Papers › MFGLib: A Library for Mean-Field Games
MFGLib: A Library for Mean-Field Games
Xin Guo, Anran Hu, Matteo Santamaria, Mahan Tajrobehkar, Junzi Zhang
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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/.
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