Special Session 22: Recent advances in mean field games for crowd dynamics

Machine Learning For Master Equations in Mean Field Games
Mathieu Lauriere
NYU Shanghai
Peoples Rep of China
Co-Author(s):    Asaf Cohen, Ethan Zell
Abstract:
Mean field games have been introduced to study games with many players. Since their introduction, they have found numerous potential applications and the theory has been extensively developed. While forward-backward systems of partial or stochastic differential equations can be used to characterize Nash equilibria with a fixed initial distribution, the Master equation introduced by P.-L. Lions provides a tool to solve the problem globally, for any initial condition. However this equation is a partial differential equation posed on the space of measures, which raises significant challenges to solve it numerically. In this talk, we will present several computational methods that have been proposed to tackle Master equations. Theoretical convergence results and numerical experiments will be presented. Mostly based on joint work with Asaf Cohen and Ethan Zell.