Search papers, labs, and topics across Lattice.
The paper introduces Rotting Adaptive Window UCB (RAW-UCB), a novel algorithm designed to achieve near-optimal regret in both rested and restless rotting bandit problems, where arm values decay over time. RAW-UCB operates effectively without prior knowledge of whether the environment is rested or restless, or the specific type of non-stationarity. This contrasts with prior work showing the difficulty of achieving good performance across both settings with a single algorithm.
A single algorithm now solves both rested and restless rotting bandits, problems previously thought to require fundamentally different approaches.
In many application domains (e.g., recommender systems, intelligent tutoring systems), the rewards associated to the actions tend to decrease over time. This decay is either caused by the actions executed in the past (e.g., a user may get bored when songs of the same genre are recommended over and over) or by an external factor (e.g., content becomes outdated). These two situations can be modeled as specific instances of the rested and restless bandit settings, where arms are rotting (i.e., their value decrease over time). These problems were thought to be significantly different, since Levine et al. (2017) showed that state-of-the-art algorithms for restless bandit perform poorly in the rested rotting setting. In this paper, we introduce a novel algorithm, Rotting Adaptive Window UCB (RAW-UCB), that achieves near-optimal regret in both rotting rested and restless bandit, without any prior knowledge of the setting (rested or restless) and the type of non-stationarity (e.g., piece-wise constant, bounded variation). This is in striking contrast with previous negative results showing that no algorithm can achieve similar results as soon as rewards are allowed to increase. We confirm our theoretical findings on a number of synthetic and dataset-based experiments.
