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arXiv · 1911.04225

Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games

Adarsh Barik·Jean Honorio
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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 $\ell_{12}-$ block regularized method which recovers a graphical game, whose Nash equilibria are the $\epsilon$-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.

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BibTeXRIS

Adarsh Barik, Jean Honorio. 2019-11-08. Provable Computational and Statistical Guarantees for Efficient Learning of Continuous-Action Graphical Games. https://arxiv.org/abs/1911.04225

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