Linear Programming Fictitious Play algorithm for Mean Field Games with optimal stopping and absorption

Abstract

We develop the fictitious play algorithm in the context of the linear programming approach for mean field games of optimal stopping and mean field games with regular control and absorption. This algorithm allows to approximate the mean field game population dynamics without computing the value function by solving linear programming problems associated with the distributions of the players still in the game and their stopping times/controls. We show the convergence of the algorithm using the topology of convergence in measure in the space of subprobability measures, which is needed to deal with the lack of continuity of the flows of measures. Numerical examples are provided to illustrate the convergence of the algorithm.

Mean field regret in discrete time games

“…Compared to the widespread usage of regret in N -player games, the usage of MFR is still young. Thus far, MFR is mostly used for learning MFEs, e.g., [39], [12], [5], and [16]. Notably, [23] develops an algorithm based on MFR that finds ε-MFEs in the absence of uniqueness of MFEs.…”