Home Page

Papers

Submissions

News

Editorial Board

Proceedings (PMLR)

Transactions (TMLR)

Open Source Software

Search

Statistics

Login

Frequently Asked Questions

Contact Us



RSS Feed

Online Learning with Sample Path Constraints

Shie Mannor, John N. Tsitsiklis, Jia Yuan Yu; 10(20):569−590, 2009.

Abstract

We study online learning where a decision maker interacts with Nature with the objective of maximizing her long-term average reward subject to some sample path average constraints. We define the reward-in-hindsight as the highest reward the decision maker could have achieved, while satisfying the constraints, had she known Nature's choices in advance. We show that in general the reward-in-hindsight is not attainable. The convex hull of the reward-in-hindsight function is, however, attainable. For the important case of a single constraint, the convex hull turns out to be the highest attainable function. Using a calibrated forecasting rule, we provide an explicit strategy that attains this convex hull. We also measure the performance of heuristic methods based on non-calibrated forecasters in experiments involving a CPU power management problem.

[abs][pdf][bib]       
© JMLR 2009. (edit, beta)