PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Online Regret Bounds for Markov Decision Processes with Deterministic Transitions
Ronald Ortner
Theoretical Computer Science Volume 411, Number 29-30, pp. 2684-2695, 2010. ISSN 0304-3975

Abstract

We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the online regret (with respect to an (eps-)optimal policy) that are logarithmic in the number of steps taken. We also present a matching lower bound. As an application, multi-armed bandits with switching cost are considered.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4583
Deposited By:Ronald Ortner
Deposited On:13 March 2009