# Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

arXiv: https://arxiv.org/abs/2305.18784 · HTML: https://mlsanity.com/paper/2305.18784/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 20 of 20)

- [Differentially-Private Federated Linear Bandits](https://mlsanity.com/paper/2010.11425/) (2020)
- [Competing Bandits in Matching Markets](https://mlsanity.com/paper/1906.05363/) (2019)
- [Federated Bandit: A Gossiping Approach](https://mlsanity.com/paper/2010.12763/) (2020)
- [Distributed Clustering of Linear Bandits in Peer to Peer Networks](https://mlsanity.com/paper/1604.07706/) (2016)
- [Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms](https://mlsanity.com/paper/1606.00911/) (2016)
- [Social Learning in Multi Agent Multi Armed Bandits](https://mlsanity.com/paper/1910.02100/) (2019)
- [SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits](https://mlsanity.com/paper/1809.08151/) (2018)
- [Distributed Exploration in Multi-Armed Bandits](https://mlsanity.com/paper/1311.0800/) (2013)
- [The Gossiping Insert-Eliminate Algorithm for Multi-Agent Bandits](https://mlsanity.com/paper/2001.05452/) (2020)
- [Collaborative Learning of Stochastic Bandits over a Social Network](https://mlsanity.com/paper/1602.08886/) (2016)
- [Concurrent bandits and cognitive radio networks](https://mlsanity.com/paper/1404.5421/) (2014)
- [Kernel Methods for Cooperative Multi-Agent Contextual Bandits](https://mlsanity.com/paper/2008.06220/) (2020)
- [Robust Multi-Agent Multi-Armed Bandits](https://mlsanity.com/paper/2007.03812/) (2020)
- [Near-Optimal Collaborative Learning in Bandits](https://mlsanity.com/paper/2206.00121/) (2022)
- [Bayesian Algorithms for Decentralized Stochastic Bandits](https://mlsanity.com/paper/2010.10569/) (2020)
- [Competing Bandits: Learning under Competition](https://mlsanity.com/paper/1702.08533/) (2017)
- [Multi-Agent Low-Dimensional Linear Bandits](https://mlsanity.com/paper/2007.01442/) (2020)
- [When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits](https://mlsanity.com/paper/2110.04396/) (2021)
- [Distributed Online Learning via Cooperative Contextual Bandits](https://mlsanity.com/paper/1308.4568/) (2013)
- [Asymptotic Optimality for Decentralised Bandits](https://mlsanity.com/paper/2109.09427/) (2021)

## Built on by (most cited first, 4 of 4)

- [Laplacian Kernelized Bandit](https://mlsanity.com/paper/2601.00461/) (2026)
- [A Near-optimal, Scalable and Parallelizable Framework for Stochastic Bandits Robust to Adversarial Corruptions and Beyond](https://mlsanity.com/paper/2502.07514/) (2025)
- [Near Optimal Best Arm Identification for Clustered Bandits](https://mlsanity.com/paper/2505.10147/) (2025)
- [Multi-Agent Combinatorial-Multi-Armed-Bandit framework for the Submodular Welfare Problem under Bandit Feedback](https://mlsanity.com/paper/2602.16183/) (2026)

## 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.