Saeed Hadikhanloo
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View article: Learning in nonatomic games, part Ⅰ: Finite action spaces and population games
Learning in nonatomic games, part Ⅰ: Finite action spaces and population games Open
We examine the long-run behavior of a wide range of dynamics for learning in nonatomic games, in both discrete and continuous time. The class of dynamics under consideration includes fictitious play and its regularized variants, the best r…
View article: Learning in nonatomic games, Part I: Finite action spaces and population games
Learning in nonatomic games, Part I: Finite action spaces and population games Open
We examine the long-run behavior of a wide range of dynamics for learning in nonatomic games, in both discrete and continuous time. The class of dynamics under consideration includes fictitious play and its regularized variants, the best-r…
View article: Finite mean field games: fictitious play and convergence to a first\n order continuous mean field game
Finite mean field games: fictitious play and convergence to a first\n order continuous mean field game Open
In this article we consider finite Mean Field Games (MFGs), i.e. with finite\ntime and finite states. We adopt the framework introduced in Gomes Mohr and\nSouza in 2010, and study two seemly unexplored subjects. In the first one, we\nanaly…
View article: Learning in anonymous nonatomic games with applications to first-order mean field games
Learning in anonymous nonatomic games with applications to first-order mean field games Open
We introduce a model of anonymous games with the player dependent action sets. We propose several learning procedures based on the well-known Fictitious Play and Online Mirror Descent and prove their convergence to equilibrium under the cl…
View article: Learning in anonymous nonatomic games with applications to first-order\n mean field games
Learning in anonymous nonatomic games with applications to first-order\n mean field games Open
We introduce a model of anonymous games with the player dependent action\nsets. We propose several learning procedures based on the well-known Fictitious\nPlay and Online Mirror Descent and prove their convergence to equilibrium under\nthe…
View article: Learning in mean field games: The fictitious play
Learning in mean field games: The fictitious play Open
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 conver…