2024
JMLR
JMLR 2024
A Survey on Multi-player Bandits
Abstract
Due mostly to its application to cognitive radio networks, multiplayer bandits gained a lot of interest in the last decade. A considerable progress has been made on its theoretical aspect. However, the current algorithms are far from applicable and many obstacles remain between these theoretical results and a possible implementation of multiplayer bandits algorithms in real communication networks. This survey contextualizes and organizes the rich multiplayer bandits literature. In light of the existing works, some clear directions for future research appear. We believe that a further study of these different directions might lead to theoretical algorithms adapted to real-world situations. [abs] [ pdf ][ bib ] © JMLR 2024. (edit, beta)
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Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— multiplayer bandit
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Cross-Pollinator
— Machine Learning, Mathematics & Optimization, Reinforcement Learning
Authors
Topics
Machine Learning > Optimization & Theory > Distributed Learning
Mathematics & Optimization > Optimization > Stochastic Methods
Machine Learning > Learning Types > Reinforcement Learning
Machine Learning > Optimization & Theory > Online Algorithms
Mathematics & Optimization > Optimization > Distributed Learning
Machine Learning > Learning Types > Multi-Armed Bandits