Robust Decentralized Multi-armed Bandits: From Corruption-Resilience to Byzantine-Resilience

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Authors: Association for Artificial Intelligence 2026, Cheng Chen, Zicheng Hu, Yuchen Wang
Cited by: 0

Abstract

Decentralized cooperative multi-agent multi-armed bandits (DeCMA2B) consider how multiple agents collaborate in a decentralized multi-armed bandit setting. Though this problem has been extensively studied in previous works, most existing methods remain susceptible to various adversarial attacks. In this paper, we first study DeCMA2B with adversarial corruption, where an adversary can corrupt reward observations of all agents with a limited corruption budget. We propose a robust algorithm, called DeMABAR, which ensures that each agent’s individual regret only suffers an additive term proportional to the corruption budget. Then we consider a more realistic scenario where the adversary can only attack a small number of agents. Our theoretical analysis shows that the DeMABAR algorithm can also almost completely eliminate the influence of adversarial attacks and is inherently robust in the Byzantine setting, where an unknown fraction of the agents can be Byzantine, i.e., arbitrarily select arms and communicate wrong information. We also conduct numerical experiments to illustrate the robustness and effectiveness of proposed methods.

Fields of Study & Subjects

Advanced Bandit Algorithms Research Reinforcement Learning in Robotics Distributed Control Multi-Agent Systems

Publication details

Journal / source
Underline Science Inc.
Publication date
7 January 2026
Language
EN