A Batch Sequential Halving Algorithm without Performance Degradation
This work addresses computational efficiency in bandit algorithms for scenarios like distributed systems, though it is incremental as it builds on an existing method.
The paper tackles the problem of performance degradation in batch-based multi-armed bandit algorithms by introducing a batch version of Sequential Halving, showing theoretically and empirically that it maintains the original algorithm's performance under practical conditions.
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.