We consider the best-arm identification problem in generalized linear bandits: 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 goal is to identify a near-optimal arm with high probability while minimizing the number of arm pulls (i.e., the sampling budget). We propose the first algorithm for this problem and provide theoretical guarantees on its accuracy and sampling efficiency.
