QuACK: A Multipurpose Queuing Algorithm for Cooperative $k$-Armed Bandits
Benjamin Howson, Sarah Filippi, Ciara Pike-Burke

TL;DR
This paper introduces QuACK, a versatile queuing algorithm that enables any single-agent bandit algorithm to be effectively extended to multi-agent cooperative settings, maintaining regret guarantees and broad applicability.
Contribution
The paper presents a black-box reduction method that generalizes single-agent bandit algorithms to multi-agent cooperative environments with theoretical regret guarantees.
Findings
The reduction preserves regret bounds in subgaussian environments.
The approach is competitive with specialized multi-agent algorithms.
Applicable to various bandit settings like heavy-tailed and duelling bandits.
Abstract
We study the cooperative stochastic -armed bandit problem, where a network of agents collaborate to find the optimal action. In contrast to most prior work on this problem, which focuses on extending a specific algorithm to the multi-agent setting, we provide a black-box reduction that allows us to extend any single-agent bandit algorithm to the multi-agent setting. Under mild assumptions on the bandit environment, we prove that our reduction transfers the regret guarantees of the single-agent algorithm to the multi-agent setting. These guarantees are tight in subgaussian environments, in that using a near minimax optimal single-player algorithm is near minimax optimal in the multi-player setting up to an additive graph-dependent quantity. Our reduction and theoretical results are also general, and apply to many different bandit settings. By plugging in appropriate single-player…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Optimization and Search Problems · Smart Grid Energy Management
