NDLI: Bandit problems in networks: Asymptotically efficient distributed allocation rules

Content Provider	IEEE Xplore Digital Library
Author	Kar, Soummya Poor, H. Vincent Cui, Shuguang
Copyright Year	2011
Description	Author affiliation: Dept. of Electrical Engineering, Princeton University, NJ 08544, USA (Kar, Soummya; Poor, H. Vincent) \|\| Dept. of Electrical and Computer Engineering, Texas A&M University, College Station, 77843, USA (Cui, Shuguang)
Abstract	This paper studies the multi-agent bandit problem in a distributed networked setting. The setting considered assumes only one bandit (the major bandit) has accessible reward information from its samples, whereas the rest (the minor bandits) have unobservable rewards. Under the assumption that the minor bandits are aware of the sampling pattern of the major bandit (but with no direct access to its rewards), a lower bound on the expected average network regret is obtained. The lower bound resembles the logarithmic optimal regret attained in single (classical) bandit problems, but in addition is shown to scale down with the number of agents. A collaborative and adaptive distributed allocation rule DA is proposed and is shown to achieve the lower bound on the expected average regret for a connected inter-bandit communication network. In particular, it is shown that under the DA allocation rule, the minor bandits attain sub-logarithmic expected regrets as opposed to logarithmic in the single agent setting.
Starting Page	1771
Ending Page	1778
File Size	398303
Page Count	8
File Format	PDF
ISBN	9781612848006
ISSN	07431546
e-ISBN	9781612848013
e-ISBN	9781612847993
DOI	10.1109/CDC.2011.6160719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Resource management Decision making Symmetric matrices Vectors Collaboration Random variables Density measurement Partially Observable Rewards Networked Bandit Problems Distributed Allocation Rules Asymptotically Efficient
Content Type	Text
Resource Type	Article
Subject	Control and Optimization Control and Systems Engineering Modeling and Simulation

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