Regret Bounds for Restless Markov Bandits
Machine Learning
2012-10-23 v1 Optimization and Control
Machine Learning
Abstract
We consider the restless Markov bandit problem, in which the state of each arm evolves according to a Markov process independently of the learner's actions. We suggest an algorithm that after steps achieves regret with respect to the best policy that knows the distributions of all arms. No assumptions on the Markov chains are made except that they are irreducible. In addition, we show that index-based policies are necessarily suboptimal for the considered problem.
Keywords
Cite
@article{arxiv.1209.2693,
title = {Regret Bounds for Restless Markov Bandits},
author = {Ronald Ortner and Daniil Ryabko and Peter Auer and Rémi Munos},
journal= {arXiv preprint arXiv:1209.2693},
year = {2012}
}
Comments
In proceedings of The 23rd International Conference on Algorithmic Learning Theory (ALT 2012)