Ergodic behavior of control and mean field games problems depending on acceleration

TL;DR

This paper investigates the long-term behavior of first-order mean field game systems with acceleration control, establishing convergence of value functions to an ergodic constant and defining the ergodic problem via fixed-point methods.

Contribution

It introduces a novel approach to define and analyze the ergodic mean field game problem with acceleration control, overcoming controllability issues.

Findings

The time average of the value function converges to an ergodic constant.

The ergodic constant is characterized as a minimum of a Lagrangian over closed probability measures.

Existence and uniqueness of solutions to the ergodic MFG problem are established.

Abstract

The goal of this paper is to study the long time behavior of solutions of the first-order mean field game (MFG) systems with a control on the acceleration. The main issue for this is the lack of small time controllability of the problem, which prevents to define the associated ergodic mean field game problem in the standard way. To overcome this issue, we first study the long-time average of optimal control problems with control on the acceleration: we prove that the time average of the value function converges to an ergodic constant and represent this ergodic constant as a minimum of a Lagrangian over a suitable class of closed probability measure. This characterization leads us to define the ergodic MFG problem as a fixed-point problem on the set of closed probability measures. Then we also show that this MFG ergodic problem has at least one solution, that the associated ergodic constant is unique under the standard mono-tonicity assumption and that the time-average of the value function of the time-dependent MFG problem with control of acceleration converges to this ergodic constant.