Learning in anonymous nonatomic games with applications to first-order mean field games
Optimization and Control
2017-04-04 v1
Abstract
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 classical monotonicity condition. Typical examples are first-order mean field games.
Keywords
Cite
@article{arxiv.1704.00378,
title = {Learning in anonymous nonatomic games with applications to first-order mean field games},
author = {Saeed Hadikhanloo},
journal= {arXiv preprint arXiv:1704.00378},
year = {2017}
}