Proximal methods for stationary Mean Field Games with local couplings

TL;DR

This paper develops and analyzes proximal algorithms for numerically solving stationary Mean Field Games with local couplings, including models with congestion effects, providing convergence guarantees and numerical validation.

Contribution

It introduces a variational framework for finite difference schemes and compares proximal methods for solving the resulting convex optimization problems in Mean Field Games.

Findings

Proposed algorithms exhibit global convergence.

Methods are stable even as viscosity approaches zero.

Numerical experiments demonstrate effectiveness of the algorithms.

Abstract

We address the numerical approximation of Mean Field Games with local couplings. For power-like Hamiltonians, we consider both unconstrained and constrained stationary systems with density constraints in order to model hard congestion effects. For finite difference discretizations of the Mean Field Game system, we follow a variational approach. We prove that the aforementioned schemes can be obtained as the optimality system of suitably defined optimization problems. In order to prove the existence of solutions of the scheme with a variational argument, the monotonicity of the coupling term is not used, which allow us to recover general existence results. Next, assuming next that the coupling term is monotone, the variational problem is cast as a convex optimization problem for which we study and compare several proximal type methods. These algorithms have several interesting features, such as global convergence and stability with respect to the viscosity parameter, which can eventually be zero. We assess the performance of the methods via numerical experiments.