Learning in mean field games: The fictitious play
ESAIM: Control, Optimisation and Calculus of Variations, Tome 23 (2017) no. 2, pp. 569-591.

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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Accepté le :
DOI : 10.1051/cocv/2016004
Classification : 35Q91, 35F21, 49L25
Mots-clés : Mean field games, learning
Cardaliaguet, Pierre 1 ; Hadikhanloo, Saeed 2

1 Université Paris-Dauphine, PSL Research University, Ceremade, Place du Maréchal de Lattre de Tassigny 75775 Paris cedex 16, France.
2 Université Paris-Dauphine, PSL Research University, Lamsade, Place du Maréchal de Lattre de Tassigny 75775 Paris cedex 16, France.
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Cardaliaguet, Pierre; Hadikhanloo, Saeed. Learning in mean field games: The fictitious play. ESAIM: Control, Optimisation and Calculus of Variations, Tome 23 (2017) no. 2, pp. 569-591. doi : 10.1051/cocv/2016004. http://www.numdam.org/articles/10.1051/cocv/2016004/

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