This paper introduces Dynamic Bayesian Optimisation
for Multi-Arm Bandits (DBO-MAB), an algorithm that dynamically
adapts hyperparameters of multi-arm bandit algorithms using incremental
Bayesian optimisation. DBO-MAB addresses the challenge
of tuning hyperparameters in uncertain and dynamic environments,
particularly for applications like web server optimisation. It uses a
dynamic range adjustment approach based on the interquartile mean
(IQM) of observed rewards to focus the search space on promising
regions. Evaluated across diverse static and dynamic environments,
DBO-MAB outperforms state-of-the-art algorithms such as Bootstrapped
UCB and f-Discounted-Sliding-Window Thompson Sampling,
reducing average response time by ≈ 55%.