Mean field games (MFG) and mean field control (MFC) are critical classes of multiagent
models for the efficient analysis of massive populations of interacting agents. Their areas of …

A normalizing flow is an invertible mapping between an arbitrary probability distribution and
a standard normal distribution; it can be used for density estimation and statistical inference …

Mean Field Games (MFG) have been introduced to tackle games with a large number of
competing players. Considering the limit when the number of players is infinite, Nash …

We propose two algorithms for the solution of the optimal control of ergodic McKean--Vlasov
dynamics. Both algorithms are based on approximations of the theoretical solutions by …

These notes are an introduction to Mean Field Game (MFG) theory, which models differential
games involving infinitely many interacting players. We focus here on the Partial Differential …

We present APAC-Net, an alternating population and agent control neural network for
solving stochastic mean-field games (MFGs). Our algorithm is geared toward high …

We address the numerical approximation of mean field games with local couplings. For
power-like Hamiltonians, we consider a stationary system and also a system involving …

We introduce a novel framework to model and solve first-order mean field game systems
with nonlocal interactions, extending the results in [L. Nurbekyan and J. Saúde, Port. Math …

This paper is devoted to the study of multi-agent deterministic optimal control problems. We
initially provide a thorough analysis of the Lagrangian, Eulerian and Kantorovich …

In this paper we propose two new monotonicity conditions that could serve as sufficient
conditions for uniqueness of Nash equilibria in mean field games. In this study we aim for …