Conference item
Decentralized cooperative stochastic bandits
- Abstract:
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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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- Files:
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(Version of record, 407.2KB)
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(Accepted manuscript, pdf, 469.8KB)
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- Publication website:
- https://papers.nips.cc/paper/8702-decentralized-cooperative-stochastic-bandits
Authors
Bibliographic Details
- 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
Item Description
- 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
Terms of use
- Copyright holder:
- Neural Information Processing Systems Foundation
- Copyright date:
- 2019
- Rights statement:
- © 2018 Neural Information Processing Systems Foundation, Inc.
- Notes:
- This paper was presented at the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 8-14 December 2019, Vancouver, Canada. This is the publisher's version of the article. The final version is available online at: https://papers.nips.cc/paper/8702-decentralized-cooperative-stochastic-bandits.pdf
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