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Concurrent bandits and cognitive radio networks

arXiv 1404.5421 ↗First posted 22 Apr 2014cs.LG cs.MAcited by 23

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Abstract (arXiv)

We consider the problem of multiple users targeting the arms of a single multi-armed stochastic bandit. The motivation for this problem comes from cognitive radio networks, where selfish users need to coexist without any side communication between them, implicit cooperation or common control. Even the number of users may be unknown and can vary as users join or leave the network. We propose an algorithm that combines an ε-greedy learning rule with a collision avoidance mechanism. We analyze its regret with respect to the system-wide optimum and show that sub-linear regret can be obtained in this setting. Experiments show dramatic improvement compared to other algorithms for this setting.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Apr 2014.

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Citing papers per year: 2021: 10; 2022: 3; 2023: 4; 2024: 4; 2025: 0; 2026: 2.

23 papers.

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  1. 2021Federated Linear Contextual Banditscited by 61
  2. 2021Federated Multi-Armed Banditscited by 59
  3. 2021On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learningcited by 35
  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. 2021Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributionscited by 8
  8. 2023Optimal Cooperative Multiplayer Learning Bandits with Noisy Rewards and No Communicationcited by 8
  9. 2021An Instance-Dependent Analysis for the Cooperative Multi-Player Multi-Armed Banditcited by 6
  10. 2021Dynamic Spectrum Access using Stochastic Multi-User Banditscited by 5
  11. 2023Collaborative Multi-Agent Heterogeneous Multi-Armed Banditscited by 4
  12. 2024Stochastic Bandits for Egalitarian Assignmentcited by 4
11 more
  1. 2021A High Performance, Low Complexity Algorithm for Multi-Player Bandits Without Collision Sensing Informationcited by 2
  2. 2021Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Banditscited by 2
  3. 2022The Pareto Frontier of Instance-Dependent Guarantees in Multi-Player Multi-Armed Bandits with no Communicationcited by 1
  4. 2022Multi-Player Bandits Robust to Adversarial Collisionscited by 1
  5. 2024United We Stand: Decentralized Multi-Agent Planning With Attritioncited by 1
  6. 2024Competing Bandits in Decentralized Contextual Matching Marketscited by 1
  7. 2023Constant or logarithmic regret in asynchronous multiplayer banditscited by 0
  8. 2023Near-Optimal Privacy-Preserving Learning for Max-Min Fair Multi-Agent Banditscited by 0
  9. 2024Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacitiescited by 0
  10. 2026Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedbackcited by 0
  11. 2026Regret Analysis of Sleeping Competing Banditscited by 0