Approximation of Discrete-Time Infinite-Horizon Mean-Field Equilibria via Finite-Horizon Mean-Field Equilibria

Abstract

We address in this paper a fundamental question that arises in mean-field games (MFGs), namely whether mean-field equilibria (MFE) for discrete-time finite-horizon MFGs can be used to obtain approximate stationary as well as non-stationary MFE for similarly structured infinite-horizon MFGs. We provide a rigorous analysis of this relationship, and show that any accumulation point of MFE of a discounted finite-horizon MFG constitutes, under weak convergence as the time horizon goes to infinity, a non-stationary MFE for the corresponding infinite-horizon MFG. Further, under certain conditions, these non-stationary MFE converge to a stationary MFE, establishing the appealing result that finite-horizon MFE can serve as approximations for stationary MFE. Additionally, we establish improved contraction rates for iterative methods used to compute regularized MFE in finite-horizon settings, extending existing results in the literature. As a byproduct, we obtain that when two MFGs have finite-horizon MFE that are close to each other, the corresponding stationary MFE are also close. As one application of the theoretical results, we show that finite-horizon MFGs can facilitate learning-based approaches to approximate infinite-horizon MFE when system components are unknown. Under further assumptions on the Lipschitz coefficients of the regularized system components (which are stronger than contractivity of finite-horizon MFGs), we obtain exponentially decaying finite-time error bounds -- in the time horizon -- between finite-horizon non-stationary, infinite-horizon non-stationary, and stationary MFE. As a byproduct of our error bounds, we present a new uniqueness criterion for infinite-horizon nonstationary MFE beyond the available contraction results in the literature.