Learning Graphon Mean Field Games and Approximate Nash Equilibria. (arXiv:2112.01280v3 [cs.GT] UPDATED)

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 large numbers of agents. So far, results have
been largely limited to graphon mean field systems with continuous-time
diffusive or jump dynamics, typically without control and with little focus on
computational methods. We propose a novel discrete-time formulation for graphon
mean field games as the limit of non-linear dense graph …

arxiv equilibria games learning