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Decentralized cooperative stochastic bandits

Abstract:

We study a decentralized cooperative stochastic multi-armed bandit problem with K arms on a network of N agents. In our model, the reward distribution of each arm is the same for each agent and rewards are drawn independently across agents and time steps. In each round, each agent chooses an arm to play and subsequently sends a message to her neighbors. The goal is to minimize the overall regret of the entire network. We design a fully decentralized algorithm that uses an accelerated consensu...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publication website:
https://papers.nips.cc/paper/8702-decentralized-cooperative-stochastic-bandits

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Oxford college:
University College
Role:
Author
ORCID:
0000-0001-7772-4160
Publisher:
Neural Information Processing Systems Foundation Publisher's website
Host title:
Advances in Neural Information Processing Systems 32
Publication date:
2019-11-05
Acceptance date:
2019-09-03
Event title:
33rd Conference on Neural Information Processing Systems (NeurIPS 2019)
Event website:
https://nips.cc/Conferences/2019
Event start date:
2019-12-08
Event end date:
2019-12-14
Language:
English
Keywords:
Pubs id:
pubs:1069685
UUID:
uuid:89855f93-5a45-4554-9cda-7e21c22ab41d
Local pid:
pubs:1069685
Source identifiers:
1069685
Deposit date:
2019-11-03

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