Sparse Binary Zero-Sum Games
Abstract
Solving zero-sum matrix games is polynomial, because it boils down to linear programming. The approximate solving is sublinear by randomized algorithms on machines with random access memory. Algorithms working separately and independently on columns and rows have been proposed, with the same performance; these versions are compliant with matrix games with stochastic reward. [1] has proposed a new version, empirically performing better on sparse problems, i.e. cases in which the Nash equilibrium has small support. In this paper, we propose a variant, similar to their work, also dedicated to sparse problems, with provably better bounds than existing methods. We then experiment the method on a card game.
Cite
Text
Auger et al. "Sparse Binary Zero-Sum Games." Proceedings of the Sixth Asian Conference on Machine Learning, 2014.Markdown
[Auger et al. "Sparse Binary Zero-Sum Games." Proceedings of the Sixth Asian Conference on Machine Learning, 2014.](https://mlanthology.org/acml/2014/auger2014acml-sparse/)BibTeX
@inproceedings{auger2014acml-sparse,
title = {{Sparse Binary Zero-Sum Games}},
author = {Auger, David and Liu, Jianlin and Ruette, Sylkvie and Saint-Pierre, David and Teytaud, Oliver},
booktitle = {Proceedings of the Sixth Asian Conference on Machine Learning},
year = {2014},
pages = {173-188},
volume = {39},
url = {https://mlanthology.org/acml/2014/auger2014acml-sparse/}
}