Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games
Computer Science and Game Theory
2019-11-12 v1 Machine Learning
Machine Learning
Abstract
In this paper, we study the problem of learning the set of pure strategy Nash equilibria and the exact structure of a continuous-action graphical game with quadratic payoffs by observing a small set of perturbed equilibria. A continuous-action graphical game can possibly have an uncountable set of Nash euqilibria. We propose a block regularized method which recovers a graphical game, whose Nash equilibria are the -Nash equilibria of the game from which the data was generated (true game). Under a slightly stringent condition on the parameters of the true game, our method recovers the exact structure of the graphical game. Our method has a logarithmic sample complexity with respect to the number of players. It also runs in polynomial time.
Keywords
Cite
@article{arxiv.1911.04225,
title = {Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games},
author = {Adarsh Barik and Jean Honorio},
journal= {arXiv preprint arXiv:1911.04225},
year = {2019}
}