Robust Approachability and Regret Minimization in Games with Partial Monitoring
Abstract
Approachability has become a standard tool in analyzing learning algorithms in the adversarial online learning setup. We develop a variant of approachability for games where there is ambiguity in the obtained reward that belongs to a set, rather than being a single vector. Using this variant we tackle the problem of approachability in games with partial monitoring and develop simple and efficient algorithms (i.e., with constant per-step complexity) for this setup. We finally consider external and internal regret in repeated games with partial monitoring, for which we derive regret-minimizing strategies based on approachability theory.
Cite
Text
Mannor et al. "Robust Approachability and Regret Minimization in Games with Partial Monitoring." Proceedings of the 24th Annual Conference on Learning Theory, 2011.Markdown
[Mannor et al. "Robust Approachability and Regret Minimization in Games with Partial Monitoring." Proceedings of the 24th Annual Conference on Learning Theory, 2011.](https://mlanthology.org/colt/2011/mannor2011colt-robust/)BibTeX
@inproceedings{mannor2011colt-robust,
title = {{Robust Approachability and Regret Minimization in Games with Partial Monitoring}},
author = {Mannor, Shie and Perchet, Vianney and Stoltz, Gilles},
booktitle = {Proceedings of the 24th Annual Conference on Learning Theory},
year = {2011},
pages = {515-536},
volume = {19},
url = {https://mlanthology.org/colt/2011/mannor2011colt-robust/}
}