Mean field games on sparse graphs using graphons, Lp graphons and graphexes
滨苍蹿辞谤尘补濒听厂测蝉迟别尘蝉听厂别尘颈苍补谤听(滨厂厂)
Centre for Intelligent Machines (CIM) and Groupe d'Etudes et de Recherche
别苍听础苍补濒测蝉别听诲别蝉听顿别肠颈蝉颈辞苍蝉听(骋贰搁础顿)
Speaker: Kai Cui
惭别别迟颈苍驳听滨顿:听845听1388听1004听听听听听听听
笔补蝉蝉肠辞诲别:听痴滨厂厂
** Note that this is a hybrid event.
** This seminar will be projected at McConnell 437 at 平特五不中.
Abstract: In this talk, we consider mean field games on graphs in discrete time. Here, each node corresponds to a single agent, and agent interaction is through their neighborhoods. We begin by reiterating graphon mean field games on dense graphs. There, we show a computational reduction thereof to standard mean field games, as well as a propagation of chaos to motivate the limiting system. We also extend results to sparser graphs via Lp graphons, and more recently via graphex mean field games for significantly more realistic, sparse graphs. Finally, we briefly give an outlook on our recent reinforcement learning algorithms for mean field control, which could be an avenue for future graphical extensions.
Bio: I am a fifth year PhD candidate at the Self-Organizing Systems Lab under supervision of Professor Heinz Koeppl at Technische Universit盲t Darmstadt. My research focuses on multi-agent reinforcement learning, mean-field games and applications thereof. Prior to the PhD studies, I received my MSc degrees in Computer Science as well as Electrical Engineering and Information Technology at Technische Universit盲t Darmstadt.
