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 …

This paper presents a general mean-field game (GMFG) framework for simultaneous
learning and decision-making in stochastic games with a large population. It first establishes …

We introduce COT-GAN, an adversarial algorithm to train implicit generative models
optimized for producing sequential data. The loss function of this algorithm is formulated …

The main purpose of this paper is to survey some recent progresses on control theory for
stochastic distributed parameter systems, ie, systems governed by stochastic differential …

The optimal transport problem has recently developed into a powerful framework for various
applications in estimation and control. Many of the recent advances in the theory and …

We study a McKean–Vlasov optimal control problem with common noise in order to
establish the corresponding limit theory as well as the equivalence between different …

A number of researchers have introduced topological structures on the set of laws of
stochastic processes. A unifying goal of these authors is to strengthen the usual weak …

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 …

We study the optimal control of path-dependent McKean–Vlasov equations valued in Hilbert
spaces motivated by non-Markovian mean-field models driven by stochastic PDEs. We first …

In this article, the problem of nonfragile finite-time stabilization for linear discrete mean-field
stochastic systems is studied. The uncertain characteristics in control parameters are …