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Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms

arXiv 1606.00911 ↗First posted 2 Jun 2016eess.SY cs.LG cs.SYcited by 48

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Abstract (arXiv)

We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem. We extend the state-of-the-art frequentist and Bayesian algorithms for single-agent MAB problems to cooperative distributed algorithms for multi-agent MAB problems in which agents communicate according to a fixed network graph. We rely on a running consensus algorithm for each agent's estimation of mean rewards from its own rewards and the estimated rewards of its neighbors. We prove the performance of these algorithms and show that they asymptotically recover the performance of a centralized agent. Further, we rigorously characterize the influence of the communication graph structure on the decision-making performance of the group.

Key facts. Cited by 48 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Jun 2016.

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Citing papers per year: 2021: 10; 2022: 12; 2023: 8; 2024: 7; 2025: 6; 2026: 5.

48 papers.

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  1. 2021Multi-Agent Multi-Armed Bandits with Limited Communicationcited by 29
  2. 2021Provably Efficient Cooperative Multi-Agent Reinforcement Learning with Function Approximationcited by 23
  3. 2021Exploiting Heterogeneity in Robust Federated Best-Arm Identificationcited by 20
  4. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  5. 2021One More Step Towards Reality: Cooperative Bandits with Imperfect Communicationcited by 15
  6. 2022Distributed Contextual Linear Bandits with Minimax Optimal Communication Costcited by 13
  7. 2024Trustworthy Distributed AI Systems: Robustness, Privacy, and Governancecited by 12
  8. 2023Decentralized Randomly Distributed Multi-agent Multi-armed Bandit with Heterogeneous Rewardscited by 11
  9. 2022Multi-Player Multi-Armed Bandits with Finite Shareable Resources Arms: Learning Algorithms & Applicationscited by 10
  10. 2022An Efficient Algorithm for Fair Multi-Agent Multi-Armed Bandit with Low Regretcited by 10
  11. 2022Distributed Bandits with Heterogeneous Agentscited by 9
  12. 2022Linear Stochastic Bandits over a Bit-Constrained Channelcited by 9
36 more
  1. 2022Private and Byzantine-Proof Cooperative Decision-Makingcited by 9
  2. 2021When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Banditscited by 7
  3. 2023On-Demand Communication for Asynchronous Multi-Agent Banditscited by 6
  4. 2021Cooperative Stochastic Multi-agent Multi-armed Bandits Robust to Adversarial Corruptionscited by 5
  5. 2022Robust Online and Distributed Mean Estimation Under Adversarial Data Corruptioncited by 5
  6. 2021Multi-Robot Gaussian Process Estimation and Coverage: A Deterministic Sequencing Algorithm and Regret Analysiscited by 4
  7. 2022Collaborative Multi-agent Stochastic Linear Banditscited by 4
  8. 2022Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Boundscited by 4
  9. 2023Collaborative Multi-Agent Heterogeneous Multi-Armed Banditscited by 4
  10. 2022On Regret-optimal Cooperative Nonstochastic Multi-armed Banditscited by 3
  11. 2021Decentralized Upper Confidence Bound Algorithms for Homogeneous Multi-Agent Multi-Armed Banditscited by 2
  12. 2022Decentralized Stochastic Multi-Player Multi-Armed Walking Banditscited by 2
  13. 2023Communication-Efficient Collaborative Regret Minimization in Multi-Armed Banditscited by 2
  14. 2024Cooperative Multi-Agent Constrained Stochastic Linear Banditscited by 2
  15. 2021Partner-Aware Algorithms in Decentralized Cooperative Bandit Teamscited by 1
  16. 2022Anonymous Bandits for Multi-User Systemscited by 1
  17. 2023Regret Lower Bounds in Multi-agent Multi-armed Banditcited by 1
  18. 2023Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse Hypergraphscited by 1
  19. 2024Cooperative Multi-Agent Graph Bandits: UCB Algorithm and Regret Analysiscited by 1
  20. 2024Decentralized Blockchain-based Robust Multi-agent Multi-armed Banditcited by 1
  21. 2024QuACK: A Multipurpose Queuing Algorithm for Cooperative k-Armed Banditscited by 1
  22. 2024Tangential Randomization in Linear Bandits (TRAiL): Guaranteed Inference and Regret Boundscited by 1
  23. 2025Communication Bounds for the Distributed Experts Problemcited by 1
  24. 2025Multi-Agent Stage-wise Conservative Linear Banditscited by 1
  25. 2026Learning to Orchestrate Agents under Uncertaintycited by 1
  26. 2023Simple Opinion Dynamics for No-Regret Learningcited by 0
  27. 2023Distributed Optimization via Kernelized Multi-armed Banditscited by 0
  28. 2024Multi-agent Multi-armed Bandits with Stochastic Sharable Arm Capacitiescited by 0
  29. 2025Collaborative Min-Max Regret in Grouped Multi-Armed Banditscited by 0
  30. 2025On the optimal regret of collaborative personalized linear banditscited by 0
  31. 2025Distributed Algorithms for Multi-Agent Multi-Armed Bandits with Collisioncited by 0
  32. 2025Distributed Multi-Agent Bandits Over ErdHos-R'enyi Random Networkscited by 0
  33. 2026Exploiting Expertise of Non-Expert and Diverse Agents in Social Bandit Learning: A Free Energy Approachcited by 0
  34. 2026Bandit Learning in General Open Multi-agent Systemscited by 0
  35. 2026DCM Bandits: Multiplayer Information Asymmetric Cascading Bandits for Multiple Clickscited by 0
  36. 2026From Solo to Social Learning: Characterizing Recursive Social Improvement in LLMscited by 0