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 …

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 …

Motivated by the models of epidemic control in large populations, we consider a Stackelberg
mean field game model between a principal and a mean field of agents whose states evolve …

We consider an n-player symmetric stochastic game with weak interactions between the
players. Time is continuous, and the horizon and the number of states are finite. We show …

We provide an abstract framework for submodular mean field games and identify verifiable
sufficient conditions that allow us to prove the existence and approximation of strong mean …

Stochastic differential games have been used extensively to model agents' competitions in
Finance, for instance, in P2P lending platforms from the Fintech industry, the banking system …

Despite its popularity in the reinforcement learning community, a provably convergent policy
gradient method for continuous space-time control problems with nonlinear state dynamics …

This paper proposes and analyzes two neural network methods to solve the master equation
for finite-state mean field games (MFGs). Solving MFGs provides approximate Nash …

We propose and compare new global solution algorithms for continuous time
heterogeneous agent economies with aggregate shocks. First, we approximate the state …

This paper establishes that a class of $ N $-player stochastic games with singular controls,
either of bounded velocity or of finite variation, can both be approximated by mean field …