Learning Discrete-Time Major-Minor Mean Field Games
Kai Cui,
Gökçe Dayanıklı,
Mathieu Laurière
et al.
Abstract: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 homogeneous players that weakly influence each other, and cannot model major players that strongly influence other players, severely limiting the class of problems that can be handled. We propose a novel discrete time version of major-minor MFGs (M3FGs), along with a learning algorithm based on fictitious play and partitioning the prob… Show more
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