# Best Arm Identification in Generalized Linear Bandits

## TL;DR

This paper introduces the first algorithm for best-arm identification in generalized linear bandits, providing theoretical guarantees and demonstrating its effectiveness through simulations, with applications like drug design.

## Contribution

It develops a novel algorithm for best-arm identification in generalized linear bandits, extending previous linear bandit methods with theoretical analysis.

## Key findings

- Algorithm achieves near-optimal sample complexity
- Theoretical guarantees on accuracy and efficiency
- Effective performance demonstrated in simulations

## Abstract

Motivated by drug design, we consider the best-arm identification problem in generalized linear bandits. More specifically, we assume each arm has a vector of covariates, there is an unknown vector of parameters that is common across the arms, and a generalized linear model captures the dependence of rewards on the covariate and parameter vectors. The problem is to minimize the number of arm pulls required to identify an arm that is sufficiently close to optimal with a sufficiently high probability. Building on recent progress in best-arm identification for linear bandits (Xu et al. 2018), we propose the first algorithm for best-arm identification for generalized linear bandits, provide theoretical guarantees on its accuracy and sampling efficiency, and evaluate its performance in various scenarios via simulation.