Multi-agent reinforcement learning (MARL), despite its popularity and empirical success,
suffers from the curse of dimensionality. This paper builds the mathematical framework to …

A mean-field-type game is a game in which the instantaneous payoffs and/or the state
dynamics functions involve not only the state and the action profile but also the joint …

This paper is concerned with an optimal control problem driven by mean-field stochastic
differential equation, where the state is partially observed via a noisy process. A new feature …

In this paper, we investigate the interaction of two populations with a large number of
indistinguishable agents. The problem consists in two levels: the interaction between agents …

In this paper, we study a class of discrete-time mean-field games under the infinite-horizon
risk-sensitive optimality criterion. Risk sensitivity is introduced for each agent (player) via an …

In this paper, we establish necessary conditions of optimality for partially observed optimal
control problems of Mckean–Vlasov type. The system is described by a controlled stochastic …

In this paper, we consider discrete-time partially observed mean-field games with the risk-
sensitive optimality criterion. We introduce risk-sensitivity behavior for each agent via an …

The paper studies partially observed optimal control problems of general McKean–Vlasov
differential equations, in which the coefficients depend on the state of the solution process …

Multi-agent reinforcement learning methods have shown remarkable potential in solving
complex multi-agent problems but mostly lack theoretical guarantees. Recently, mean field …

The contents of this book comprise an appropriate background to start working and doing
research on mean-field-type control and game theory. To make the exposition and …