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 …

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 …

Although the field of multi-agent reinforcement learning (MARL) has made considerable
progress in the last years, solving systems with a large number of agents remains a hard …

Recent advances at the intersection of dense large graph limits and mean field games have
begun to enable the scalable analysis of a broad class of dynamical sequential games with …

This paper studies stochastic games on large graphs and their graphon limits. We propose a
new formulation of graphon games based on a single typical player's label-state distribution …

We consider a game for a continuum of non-identical players evolving on a finite state
space. Their heterogeneous interactions are represented with a graphon, which can be …

We study high-dimensional stochastic optimal control problems in which many agents
cooperate to minimize a convex cost functional. We consider both the full-information …

Green AI (Artificial Intelligence) and digitalization facilitate the “Dual-Carbon” goal of low-
carbon, high-quality economic development. Green AI is moving from “cloud” to “edge” …

In this paper, we study graphon mean field games using a system of forward–backward
stochastic differential equations. We establish the existence and uniqueness of solutions …

Potential game is an emerging notion and framework for studying multi-agent games,
especially with heterogeneous agents. Up to date, potential games have been extensively …