Sample Complexity of an Adversarial Attack on UCB-based Best-arm Identification Policy
Not on the map. Why?
Abstract (arXiv)
In this work I study the problem of adversarial perturbations to rewards, in a Multi-armed bandit (MAB) setting. Specifically, I focus on an adversarial attack to a UCB type best-arm identification policy applied to a stochastic MAB. The UCB attack presented in [1] results in pulling a target arm K very often. I used the attack model of [1] to derive the sample complexity required for selecting target arm K as the best arm. I have proved that the stopping condition of UCB based best-arm identification algorithm given in [2], can be achieved by the target arm K in T rounds, where T depends only on the total number of arms and σ parameter of σ2- sub-Gaussian random rewards of the arms.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 11 references that are in the map. First posted on arXiv 13 Sep 2022.
Builds on
6 of 11 references.
References · 6
- 2014Explaining and Harnessing Adversarial Examplescited by 7,287
- 2017Tactics of Adversarial Attack on Deep Reinforcement Learning Agentscited by 163
- 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 152
- 2013lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Banditscited by 148
- 2014Algorithms for multi-armed bandit problemscited by 63
- 2018Adversarial Attacks on Stochastic Banditscited by 56
Built on by
Not cited yet.