Tightening Regret Lower and Upper Bounds in Restless Rising Bandits

Restless Multi-Armed Bandits (MABs) are a general framework designed to handle real-world decision-making problems where the expected rewards evolve over time, such as in recommender systems and dynamic pricing. In this work, we investigate from a theoretical standpoint two well-known structured subclasses of restless MABs: the rising and the rising concave settings, where the expected reward of each arm evolves over time following an unknown non-decreasing and a non-decreasing concave function, respectively. By providing a novel methodology of independent interest for general restless bandits, we establish new lower bounds on the expected cumulative regret for both settings. In the rising case, we prove a lower bound of order ΩpT2{3q, matching known upper bounds for restless bandits; whereas, in the rising concave case, we derive a lower bound of order ΩpT3{5q, proving for the first time that this setting is provably more challenging than stationary MABs. Then, we introduce Rising Concave Budgeted Exploration (RC-BEpαq), a new regret minimization algorithm designed for the rising concave MABs. By devising a novel proof technique, we show that the expected cumulative regret of RC-BEpαq is in the order of rOpT7{11q. These results collectively make a step towards closing the gap in rising concave MABs, positioning them between stationary and general restless bandit settings in terms of statistical complexity.