Regret Analysis for Hierarchical Experts Bandit Problem
This work addresses decision-making in multi-layered expert systems for bandit problems, offering incremental theoretical insights.
The paper tackles the hierarchical experts bandit problem by analyzing regret growth across multiple layers and deriving sub-linear upper bounds for UCB strategies, with experimental validation showing practical significance.
We study an extension of standard bandit problem in which there are R layers of experts. Multi-layered experts make selections layer by layer and only the experts in the last layer can play arms. The goal of the learning policy is to minimize the total regret in this hierarchical experts setting. We first analyze the case that total regret grows linearly with the number of layers. Then we focus on the case that all experts are playing Upper Confidence Bound (UCB) strategy and give several sub-linear upper bounds for different circumstances. Finally, we design some experiments to help the regret analysis for the general case of hierarchical UCB structure and show the practical significance of our theoretical results. This article gives many insights about reasonable hierarchical decision structure.