mlsanityData to 14 Oct 2026

A survey on multi-player bandits

arXiv 2211.16275 ↗First posted 29 Nov 2022stat.ML cs.GT cs.LGcited by 19

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Abstract (arXiv)

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 cognitive radio 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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 53 of its 135 references that are in the map. First posted on arXiv 29 Nov 2022.

Builds on

53 of 135 references.

References · 53

  1. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 567
  2. 2019Introduction to Multi-Armed Banditscited by 481
  3. 2018Thompson Sampling for Combinatorial Semi-Banditscited by 78
  4. 2016Delay and Cooperation in Nonstochastic Banditscited by 71
  5. 2019Competing Bandits in Matching Marketscited by 56
  6. 2016On Explore-Then-Commit Strategiescited by 47
  7. 2018SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Banditscited by 47
  8. 2020Bandit Learning in Decentralized Matching Marketscited by 46
  9. 2015Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple Playscited by 38
  10. 2021Learning Equilibria in Matching Markets from Bandit Feedbackcited by 34
  11. 2015On Distributed Cooperative Decision-Making in Multiarmed Banditscited by 33
  12. 2020Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Banditscited by 31
38 more
  1. 2021Beyond log2(T) Regret for Decentralized Bandits in Matching Marketscited by 30
  2. 2020The Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Armscited by 28
  3. 2019A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Playerscited by 25
  4. 2020Decentralized Multi-player Multi-armed Bandits with No Collision Informationcited by 25
  5. 2014Concurrent bandits and cognitive radio networkscited by 24
  6. 2016Combinatorial semi-bandit with known covariancecited by 24
  7. 2019Cooperative Online Learning: Keeping your Neighbors Updatedcited by 22
  8. 2015On Regret-Optimal Learning in Decentralized Multi-player Multi-armed Banditscited by 20
  9. 2018Multiplayer bandits without observing collision informationcited by 20
  10. 2019Individual Regret in Cooperative Nonstochastic Multi-Armed Banditscited by 20
  11. 2020Robust Multi-Agent Multi-Armed Banditscited by 20
  12. 2021Regret, stability & fairness in matching markets with bandit learnerscited by 20
  13. 2021Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalizationcited by 18
  14. 2018Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settingscited by 17
  15. 2019Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Withoutcited by 14
  16. 2020Selfish Robustness and Equilibria in Multi-Player Banditscited by 13
  17. 2020Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisionscited by 13
  18. 2021Distributed Learning over Markovian Fading Channels for Stable Spectrum Accesscited by 13
  19. 2018Game of Thrones: Fully Distributed Learning for Multi-Player Banditscited by 12
  20. 2019Multiplayer Bandit Learning, from Competition to Cooperationcited by 12
  21. 2019Multi-Player Bandits: The Adversarial Casecited by 11
  22. 2020Coordination without communication: optimal regret in two players multi-armed banditscited by 11
  23. 2021Towards Optimal Algorithms for Multi-Player Bandits without Collision Sensing Informationcited by 11
  24. 2019Multi-User MABs with User Dependent Rewards for Uncoordinated Spectrum Accesscited by 10
  25. 2018Multi-User Multi-Armed Bandits for Uncoordinated Spectrum Accesscited by 9
  26. 2018Multi-user Communication Networks: A Coordinated Multi-armed Bandit Approachcited by 8
  27. 2020Stability and Learning in Strategic Queuing Systemscited by 8
  28. 2021Decentralized Learning in Online Queuing Systemscited by 8
  29. 2020Dominate or Delete: Decentralized Competing Bandits in Serial Dictatorshipcited by 7
  30. 2021Be Greedy in Multi-Armed Banditscited by 7
  31. 2020An Efficient Algorithm for Cooperative Semi-Banditscited by 6
  32. 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Banditcited by 6
  33. 2020On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communicationscited by 5
  34. 2021Dynamic Spectrum Access using Stochastic Multi-User Banditscited by 5
  35. 2018Learning to Coordinate in a Decentralized Cognitive Radio Network in Presence of Jammerscited by 4
  36. 2020Virtues of Patience in Strategic Queuing Systemscited by 4
  37. 2021Asymptotically Optimal Strategies For Combinatorial Semi-Bandits in Polynomial Timecited by 4
  38. 2021Distributed learning in congested environments with partial informationcited by 4

50 most cited of 53 All 53 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 5; 2026: 5.

19 papers.

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  1. 2024Incentivized Learning in Principal-Agent Bandit Gamescited by 13
  2. 2023Competing for Shareable Arms in Multi-Player Multi-Armed Banditscited by 6
  3. 2025Choosing the Better Bandit Algorithm under Data Sharing: When Do A/B Experiments Work?cited by 4
  4. 2024Learning to Mitigate Externalities: the Coase Theorem with Hindsight Rationalitycited by 3
  5. 2024Multi-Player Approaches for Dueling Banditscited by 2
  6. 2025KernelBand: Steering LLM-based Kernel Optimization via Hardware-Aware Multi-Armed Banditscited by 2
  7. 2024GROS: A General Robust Aggregation Strategycited by 1
  8. 2024QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Banditscited by 1
  9. 2024Competing Bandits in Decentralized Contextual Matching Marketscited by 1
  10. 2025Bandit Max-Min Fair Allocationcited by 1
  11. 2026Multi-Agent Lipschitz Banditscited by 1
  12. 2026Regret Analysis of Sleeping Competing Banditscited by 1
7 more
  1. 2023Constant or logarithmic regret in asynchronous multiplayer banditscited by 0
  2. 2024PPA-Game: Characterizing and Learning Competitive Dynamics Among Online Content Creatorscited by 0
  3. 2025In-Domain African Languages Translation Using LLMs and Multi-armed Banditscited by 0
  4. 2025Decentralized Asynchronous Multi-player Banditscited by 0
  5. 2026Scaling Federated Linear Contextual Bandits via Sketchingcited by 0
  6. 2026Coordinating the Unknown Lipschitz Constant in Multiplayer Banditscited by 0
  7. 2026Approximating Optimal Policies in Heterogeneous Asynchronous Multi-player Banditscited by 0