mlsanityData to 7 Oct 2026

Decentralized Multi-player Multi-armed Bandits with No Collision Information

arXiv 2003.00162 ↗First posted 29 Feb 2020cs.LG cs.IT math.ITcited by 25

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Abstract (arXiv)

The decentralized stochastic multi-player multi-armed bandit (MP-MAB) problem, where the collision information is not available to the players, is studied in this paper. Building on the seminal work of Boursier and Perchet (2019), we propose error correction synchronization involving communication (EC-SIC), whose regret is shown to approach that of the centralized stochastic MP-MAB with collision information. By recognizing that the communication phase without collision information corresponds to the Z-channel model in information theory, the proposed EC-SIC algorithm applies optimal error correction coding for the communication of reward statistics. A fixed message length, as opposed to the logarithmically growing one in Boursier and Perchet (2019), also plays a crucial role in controlling the communication loss. Experiments with practical Z-channel codes, such as repetition code, flip code and modified Hamming code, demonstrate the superiority of EC-SIC in both synthetic and real-world datasets.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Feb 2020.

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Citing papers per year: 2021: 10; 2022: 4; 2023: 6; 2024: 3; 2025: 1; 2026: 1.

25 papers.

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  1. 2021Federated Multi-armed Bandits with Personalizationcited by 62
  2. 2021Federated Linear Contextual Banditscited by 61
  3. 2021Federated Multi-Armed Banditscited by 59
  4. 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalizationcited by 18
  5. 2022A survey on multi-player banditscited by 18
  6. 2021Online Learning for Cooperative Multi-Player Multi-Armed Banditscited by 12
  7. 2021Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Informationcited by 11
  8. 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributionscited by 8
  9. 2023Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communicationcited by 8
  10. 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Banditcited by 6
  11. 2023Competing for Shareable Arms in Multi-Player Multi-Armed Banditscited by 6
  12. 2021Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptionscited by 5
13 more
  1. 2023Incentivized Communication for Federated Banditscited by 3
  2. 2021A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Informationcited by 2
  3. 2022Decentralized Stochastic Multi-Player Multi-Armed Walking Banditscited by 2
  4. 2024Incentivized Truthful Communication for Federated Banditscited by 2
  5. 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communicationcited by 1
  6. 2022Multi-Player Bandits Robust to Adversarial Collisionscited by 1
  7. 2023Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphscited by 1
  8. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  9. 2023Constant or logarithmic regret in asynchronous multiplayer banditscited by 0
  10. 2024PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creatorscited by 0
  11. 2024Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacitiescited by 0
  12. 2025Decentralized Asynchronous Multi-player Banditscited by 0
  13. 2026The Price of Decentralization in Top-K Arm Identificationcited by 0