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INDEXABILITY OF BANDIT PROBLEMS WITH RESPONSE DELAYS

Published online by Cambridge University Press:  23 April 2010

Felipe Caro  and
Onesun Steve Yoo
Felipe Caro
Affiliation:
UCLA Anderson School of Management Los Angeles, CA 90095, E-mail: [email protected]; [email protected]
Onesun Steve Yoo
Affiliation:
UCLA Anderson School of Management Los Angeles, CA 90095, E-mail: [email protected]; [email protected]
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Abstract

This article considers an important class of discrete time restless bandits, given by the discounted multiarmed bandit problems with response delays. The delays in each period are independent random variables, in which the delayed responses do not cross over. For a bandit arm in this class, we use a coupling argument to show that in each state there is a unique subsidy that equates the pulling and nonpulling actions (i.e., the bandit satisfies the indexibility criterion introduced by Whittle (1988). The result allows for infinite or finite horizon and holds for arbitrary delay lengths and infinite state spaces. We compute the resulting marginal productivity indexes (MPI) for the Beta-Bernoulli Bayesian learning model, formulate and compute a tractable upper bound, and compare the suboptimality gap of the MPI policy to those of other heuristics derived from different closed-form indexes. The MPI policy performs near optimally and provides a theoretical justification for the use of the other heuristics.

Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 24 , Issue 3 , July 2010 , pp. 349 - 374
Copyright
Copyright © Cambridge University Press 2010

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