Distributed Learning in Multi-Armed Bandit With Multiple Players

We formulate and study a decentralized multi-armed bandit (MAB) problem. There are M distributed players competing for N independent arms. Each arm, when played, offers i.i.d. reward according to a distribution with an unknown parameter. At each time, each player chooses one arm to play without exch...

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Veröffentlicht in:	IEEE transactions on signal processing 2010-11, Vol.58 (11), p.5667-5681
Hauptverfasser:	Liu, Keqin, Zhao, Qing
Format:	Artikel
Sprache:	eng
Schlagworte:	Advertising Applied sciences Arm Cognitive radio Construction Decentralized decentralized multi-armed bandit distributed learning Exact sciences and technology Exchanging Information, signal and communications theory Laboratories Miscellaneous multi-agent systems Networks Optimization Permission Players Policies Radio access networks Radio networks Searching Signal processing Social network services Studies system regret TDF Telecommunications and information theory USA Councils Web search Web search and advertising
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