  In this paper, we investigate the problem of pure exploration in the context
of multi-armed bandits, with a specific focus on scenarios where arms are
pulled in fixed-size batches. Batching has been shown to enhance computational
efficiency, but it can potentially lead to a degradation compared to the
original sequential algorithm's performance due to delayed feedback and reduced
adaptability. We introduce a simple batch version of the Sequential Halving
(SH) algorithm (Karnin et al., 2013) and provide theoretical evidence that
batching does not degrade the performance of the original algorithm under
practical conditions. Furthermore, we empirically validate our claim through
experiments, demonstrating the robust nature of the SH algorithm in fixed-size
batch settings.
