12 citations · 30 across the 23 of their papers we have counts for
5 papers · 1 filter
Approximately Solving Continuous-Time Mean Field Games with Finite State Spaces
Yannick Eich, Christian Fabian, Kai Cui +1
Mean field games (MFGs) offer a powerful framework for modeling large-scale multi-agent systems. This paper addresses MFGs formulated in continuous time with discrete state spaces,…
Bounded Rationality Equilibrium Learning in Mean Field Games
Yannick Eich, Christian Fabian, Kai Cui +1
Mean field games (MFGs) tractably model behavior in large agent populations. The literature on learning MFG equilibria typically focuses on finding Nash equilibria (NE), which assu…
Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui, Gökçe Dayanıklı, Mathieu Laurière +3
Recent techniques based on Mean Field Games (MFGs) allow the scalable analysis of multi-player games with many similar, rational agents. However, standard MFGs remain limited to ho…
Hypergraphon Mean Field Games
Kai Cui, Wasiur R. KhudaBukhsh, Heinz Koeppl
We propose an approach to modelling large-scale multi-agent dynamical systems allowing interactions among more than just pairs of agents using the theory of mean field games and th…
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Kai Cui, Heinz Koeppl
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…