Stochastic optimal control and games have a wide range of applications, from finance and
economics to social sciences, robotics, and energy management. Many real-world …

In this paper, we deepen the analysis of continuous time Fictitious Play learning algorithm to
the consideration of various finite state Mean Field Game settings (finite horizon, $\gamma …

Abstract Mean Field Games (MFGs) have been introduced to efficiently approximate games
with very large populations of strategic agents. Recently, the question of learning equilibria …

Non-cooperative and cooperative games with a very large number of players have many
applications but remain generally intractable when the number of players increases …

We address scaling up equilibrium computation in Mean Field Games (MFGs) using Online
Mirror Descent (OMD). We show that continuous-time OMD provably converges to a Nash …

We propose a single-level numerical approach to solve Stackelberg mean field game (MFG)
problems. In the Stackelberg MFG, an infinite population of agents plays a noncooperative …

This paper studies two fundamental problems in regularized Graphon Mean-Field Games
(GMFGs). First, we establish the existence of a Nash Equilibrium (NE) of any $\lambda …

Mean field games (MFG) and mean field control problems (MFC) are frameworks to study
Nash equilibria or social optima in games with a continuum of agents. These problems can …

Mean-field games (MFGs) have shown strong modeling capabilities for large systems in
various fields, driving growth in computational methods for mean-field game problems …

With the invention of the COVID-19 vaccine, shipping and distributing are crucial in
controlling the pandemic. In this paper, we build a mean-field variational problem in a spatial …