Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits

Ronshee Chawla, Daniel Vial, Sanjay Shakkottai, R. Srikant

Research output: Contribution to journalConference articlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)4189-4217
Number of pages29
JournalProceedings of Machine Learning Research
StatePublished - 2023
Externally publishedYes
Event40th International Conference on Machine Learning, ICML 2023 - Honolulu, United States
Duration: Jul 23 2023Jul 29 2023

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


Dive into the research topics of 'Collaborative Multi-Agent Heterogeneous Multi-Armed Bandits'. Together they form a unique fingerprint.

Cite this