12 citations · 30 across the 23 of their papers we have counts for
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cs.GT2021
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…
cs.LG2021
Discrete-Time Mean Field Control with Environment States
Kai Cui, Anam Tahir, Mark Sinzger +1
Multi-agent reinforcement learning methods have shown remarkable potential in solving complex multi-agent problems but mostly lack theoretical guarantees. Recently, mean field cont…
cs.MA2021★ 12 cited
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
Kai Cui, Heinz Koeppl
The recent mean field game (MFG) formalism facilitates otherwise intractable computation of approximate Nash equilibria in many-agent settings. In this paper, we consider discrete-…