mlsanityData to 7 Oct 2026

Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

arXiv 2305.18784 ↗First posted 30 May 2023cs.LG cs.DC cs.MAcited by 4

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Abstract (arXiv)

The study of collaborative multi-agent bandits has attracted significant attention recently. In light of this, we initiate the study of a new collaborative setting, consisting of N agents such that each agent is learning one of M stochastic multi-armed bandits to minimize their group cumulative regret. We develop decentralized algorithms which facilitate collaboration between the agents under two scenarios. We characterize the performance of these algorithms by deriving the per agent cumulative regret and group regret upper bounds. We also prove lower bounds for the group regret in this setting, which demonstrates the near-optimal behavior of the proposed algorithms.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 20 of its 39 references that are in the map. First posted on arXiv 30 May 2023.

Builds on

20 of 39 references.

References · 20

  1. 2020Differentially-Private Federated Linear Banditscited by 83
  2. 2020Federated Bandit: A Gossiping Approachcited by 55
  3. 2019Competing Bandits in Matching Marketscited by 54
  4. 2016Distributed Clustering of Linear Bandits in Peer to Peer Networkscited by 50
  5. 2016Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithmscited by 48
  6. 2019Social Learning in Multi Agent Multi Armed Banditscited by 48
  7. 2013Distributed Exploration in Multi-Armed Banditscited by 46
  8. 2018SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Banditscited by 46
  9. 2020The Gossiping Insert-Eliminate Algorithm for Multi-Agent Banditscited by 37
  10. 2016Collaborative Learning of Stochastic Bandits over a Social Networkcited by 29
  11. 2014Concurrent bandits and cognitive radio networkscited by 23
  12. 2020Kernel Methods for Cooperative Multi-Agent Contextual Banditscited by 21
8 more
  1. 2020Robust Multi-Agent Multi-Armed Banditscited by 20
  2. 2022Near-Optimal Collaborative Learning in Banditscited by 18
  3. 2020Bayesian Algorithms for Decentralized Stochastic Banditscited by 11
  4. 2017Competing Bandits: Learning under Competitioncited by 9
  5. 2020Multi-Agent Low-Dimensional Linear Banditscited by 8
  6. 2021When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Banditscited by 7
  7. 2013Distributed Online Learning via Cooperative Contextual Banditscited by 5
  8. 2021Asymptotic Optimality for Decentralised Banditscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyondcited by 0
  2. 2025Near Optimal Best Arm Identification for Clustered Banditscited by 0
  3. 2026Laplacian Kernelized Banditcited by 0
  4. 2026Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedbackcited by 0