Abstract
Mean Field Game systems describe equilibrium configurations in differential games with infinitely many infinitesimal interacting agents. We introduce a learning procedure (similar to the Fictitious Play) for these games and show its convergence when the Mean Field Game is potential.
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APA
Cardaliaguet, P., & Hadikhanloo, S. (2017). Learning in mean field games: The fictitious play. ESAIM - Control, Optimisation and Calculus of Variations, 23(2), 569–591. https://doi.org/10.1051/cocv/2016004
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