Geodesic
Learning in mean field games: The fictitious play
Cardaliaguet, Pierre1  ; Hadikhanloo, Saeed2 
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.
ESAIM: Control, Optimisation and Calculus of Variations, Tome 23 (2017) no. 2, pp. 569-591 Cet article a éte moissonné depuis la source Numdam

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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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DOI : 10.1051/cocv/2016004
Classification : 35Q91, 35F21, 49L25
Keywords: 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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     title = {Learning in mean field games: {The} fictitious play},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     pages = {569--591},
     year = {2017},
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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

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