Library Header Image
LSE Research Online LSE Library Services

On regret-optimal cooperative nonstochastic multi-armed bandits

Yi, Jialin and Vojnović, Milan (2023) On regret-optimal cooperative nonstochastic multi-armed bandits. Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, 2023-M. pp. 1329-1335. ISSN 1548-8403

Full text not available from this repository.


We consider the nonstochastic multi-agent multi-armed bandit problem with agents collaborating via a communication network with delays. We show a lower bound for individual regret of all agents. We show that with suitable regularizers and communication protocols, a collaborative multi-agent follow-the-regularized-leader (FTRL) algorithm has an individual regret upper bound that matches the lower bound up to a constant factor when the number of arms is large enough relative to degrees of agents in the communication graph. We also show that an FTRL algorithm with a suitable regularizer is regret optimal with respect to the scaling with the edge-delay parameter. We present numerical experiments validating our theoretical results and demonstrate cases when our algorithms outperform previously proposed algorithms.

Item Type: Article
Additional Information: © 2023 International Foundation for Autonomous Agents and Multiagent Systems
Divisions: LSE
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 24 May 2024 11:36
Last Modified: 20 Jul 2024 05:27

Actions (login required)

View Item View Item