Foolproof Cooperative Learning
Abstract
This paper extends the notion of learning algorithms and learning equilibriums from repeated games theory to stochastic games. We introduce Foolproof Cooperative Learning (FCL), an algorithm that converges to an equilibrium strategy that allows cooperative strategies in self-play setting while being not exploitable by selfish learners. By construction, FCL is a learning equilibrium for repeated symmetric games. We illustrate the behavior of FCL on symmetric matrix and grid games, and its robustness to selfish learners.
Cite
Text
Jacq et al. "Foolproof Cooperative Learning." Proceedings of The 12th Asian Conference on Machine Learning, 2020.Markdown
[Jacq et al. "Foolproof Cooperative Learning." Proceedings of The 12th Asian Conference on Machine Learning, 2020.](https://mlanthology.org/acml/2020/jacq2020acml-foolproof/)BibTeX
@inproceedings{jacq2020acml-foolproof,
title = {{Foolproof Cooperative Learning}},
author = {Jacq, Alexis and Perolat, Julien and Geist, Matthieu and Pietquin, Olivier},
booktitle = {Proceedings of The 12th Asian Conference on Machine Learning},
year = {2020},
pages = {401-416},
volume = {129},
url = {https://mlanthology.org/acml/2020/jacq2020acml-foolproof/}
}