Robust approachability and regret minimization in games with partial monitoring

Shie Mannor, Vianney Perchet, Gilles Stoltz
Proceedings of the 24th Annual Conference on Learning Theory, PMLR 19:515-536, 2011.

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 this Paper

BibTeX
@InProceedings{pmlr-v19-mannor11a, 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}, pages = {515--536}, year = {2011}, editor = {Kakade, Sham M. and von Luxburg, Ulrike}, volume = {19}, series = {Proceedings of Machine Learning Research}, address = {Budapest, Hungary}, month = {09--11 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v19/mannor11a/mannor11a.pdf}, url = {https://proceedings.mlr.press/v19/mannor11a.html}, 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.} }
Endnote
%0 Conference Paper %T Robust approachability and regret minimization in games with partial monitoring %A Shie Mannor %A Vianney Perchet %A Gilles Stoltz %B Proceedings of the 24th Annual Conference on Learning Theory %C Proceedings of Machine Learning Research %D 2011 %E Sham M. Kakade %E Ulrike von Luxburg %F pmlr-v19-mannor11a %I PMLR %P 515--536 %U https://proceedings.mlr.press/v19/mannor11a.html %V 19 %X 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.
RIS
TY - CPAPER TI - Robust approachability and regret minimization in games with partial monitoring AU - Shie Mannor AU - Vianney Perchet AU - Gilles Stoltz BT - Proceedings of the 24th Annual Conference on Learning Theory DA - 2011/12/21 ED - Sham M. Kakade ED - Ulrike von Luxburg ID - pmlr-v19-mannor11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 19 SP - 515 EP - 536 L1 - http://proceedings.mlr.press/v19/mannor11a/mannor11a.pdf UR - https://proceedings.mlr.press/v19/mannor11a.html AB - 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. ER -
APA
Mannor, S., Perchet, V. & Stoltz, G.. (2011). Robust approachability and regret minimization in games with partial monitoring. Proceedings of the 24th Annual Conference on Learning Theory, in Proceedings of Machine Learning Research 19:515-536 Available from https://proceedings.mlr.press/v19/mannor11a.html.

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