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Some memoryless bandit policies

Part of: Sequential methods Operations research and management science

Published online by Cambridge University Press:  14 July 2016

Erol A. Peköz
Erol A. Peköz*
Affiliation:
Boston University
*
∗ Postal address: School of Management, Boston University, Boston, MA 02215, USA. Email address: [email protected]
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Abstract

We consider a multiarmed bandit problem, where each arm when pulled generates independent and identically distributed nonnegative rewards according to some unknown distribution. The goal is to maximize the long-run average reward per pull with the restriction that any previously learned information is forgotten whenever a switch between arms is made. We present several policies and a peculiarity surrounding them.

Keywords

Bandit problemmultiarmed bandit problemaverage reward criteria

MSC classification

Primary: 62L05: Sequential design 90B36: Scheduling theory, stochastic 62L15: Optimal stopping
Type
Short Communications
Information
Journal of Applied Probability , Volume 40 , Issue 1 , March 2003 , pp. 250 - 256
Copyright
Copyright © Applied Probability Trust 2003 

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