2025/09/02 by Chanakya Varude, Jay Chaudhary, Varude, Chanakya +5
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #FOS: Computer and information sciences #Game Theory and Applications #Machine Learning (cs.LG) #Stochastic Gradient Optimization Techniques
paper · pdf · doi:10.48550/arxiv.2509.02119
openalex publication_date 2025/09/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In multi-armed bandit problems, the typical goal is to identify the arm with the highest reward. This paper explores a threshold-based bandit problem, aiming to select an arm based on its relation to a prescribed threshold \(τ\). We study variants where the optimal arm is the first above \(τ\), the \(kth\) arm above or below it, or the closest to it, under a monotonic structure of arm means. We derive asymptotic regret lower bounds, showing dependence only on arms adjacent to \(τ\). Motivated by applications in communication networks (CQI allocation), clinical dosing, energy management, recommendation systems, and more. We propose algorithms with optimality validated through Monte Carlo simulations. Our work extends classical bandit theory with threshold constraints for efficient decision-making.