Asymptotic Optimality for Decentralised Bandits

Conor Newton; A J Ganesh; Henry Reeve

doi:10.1145/3512798.3512817

Asymptotic Optimality for Decentralised Bandits

Conor Newton, A J Ganesh, Henry Reeve

Research output: Contribution to journal › Article (Academic Journal) › peer-review

3 Citations (Scopus)

17 Downloads (Pure)

Abstract

We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. We present an algorithm which improves upon the Gossip- Insert-Eliminate method of Chawla et al. [3]. We provide a regret bound which shows that our algorithm is asymptotically optimal and present empirical results demonstrating lower regret on simulated data.

Original language	English
Pages (from-to)	51-53
Number of pages	3
Journal	ACM SIGMETRICS Performance Evaluation Review
Volume	49
Issue number	2
DOIs	https://doi.org/10.1145/3512798.3512817
Publication status	Published - 20 Jan 2022
Event	Reinforcement Learning in Networks and Queues, Sigmetrics 2021 - Duration: 14 Jun 2021 → 14 Jun 2021

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3 Citations
1 Article (Academic Journal)

Asymptotic Optimality for Decentralised Bandits
Newton, C. J., Ganesh, A. & Reeve, H. W. J., 1 Mar 2023, In: Dynamic Games and Applications. 13, p. 307-325
Research output: Contribution to journal › Article (Academic Journal) › peer-review

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91 Downloads (Pure)

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abstract = "We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. We present an algorithm which improves upon the Gossip- Insert-Eliminate method of Chawla et al. [3]. We provide a regret bound which shows that our algorithm is asymptotically optimal and present empirical results demonstrating lower regret on simulated data.",

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AU - Reeve, Henry

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AB - We consider a large number of agents collaborating on a multi-armed bandit problem with a large number of arms. We present an algorithm which improves upon the Gossip- Insert-Eliminate method of Chawla et al. [3]. We provide a regret bound which shows that our algorithm is asymptotically optimal and present empirical results demonstrating lower regret on simulated data.

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DO - 10.1145/3512798.3512817

M3 - Article (Academic Journal)

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T2 - Reinforcement Learning in Networks and Queues, Sigmetrics 2021

Y2 - 14 June 2021 through 14 June 2021

ER -

Asymptotic Optimality for Decentralised Bandits

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Asymptotic Optimality for Decentralised Bandits

Cite this