Best-Arm Identification in Correlated Multi-Armed Bandits

TL;DR

This paper introduces C-LUCB, a new algorithm for best-arm identification in correlated multi-armed bandits that leverages correlation knowledge to significantly reduce sample complexity compared to traditional methods.

Contribution

The paper proposes a novel correlated bandit framework and the C-LUCB algorithm, which exploits correlation information to improve sample efficiency in best-arm identification.

Findings

C-LUCB reduces sample complexity by focusing on a subset of competitive arms.

Theoretical analysis shows sample complexity depends on the size of the set of competitive arms.

Experimental results on recommendation datasets validate the efficiency of C-LUCB.

Abstract

In this paper we consider the problem of best-arm identification in multi-armed bandits in the fixed confidence setting, where the goal is to identify, with probability $1-\delta$ for some $\delta>0$, the arm with the highest mean reward in minimum possible samples from the set of arms $\mathcal{K}$. Most existing best-arm identification algorithms and analyses operate under the assumption that the rewards corresponding to different arms are independent of each other. We propose a novel correlated bandit framework that captures domain knowledge about correlation between arms in the form of upper bounds on expected conditional reward of an arm, given a reward realization from another arm. Our proposed algorithm C-LUCB, which generalizes the LUCB algorithm utilizes this partial knowledge of correlations to sharply reduce the sample complexity of best-arm identification. More interestingly, we show that the total samples obtained by C-LUCB are of the form $\mathcal{O}\left(\sum_{k \in \mathcal{C}} \log\left(\frac{1}{\delta}\right)\right)$ as opposed to the typical $\mathcal{O}\left(\sum_{k \in \mathcal{K}} \log\left(\frac{1}{\delta}\right)\right)$ samples required in the independent reward setting. The improvement comes, as the $\mathcal{O}(\log(1/\delta))$ term is summed only for the set of competitive arms $\mathcal{C}$, which is a subset of the original set of arms $\mathcal{K}$. The size of the set $\mathcal{C}$, depending on the problem setting, can be as small as $2$, and hence using C-LUCB in the correlated bandits setting can lead to significant performance improvements. Our theoretical findings are supported by experiments on the Movielens and Goodreads recommendation datasets.