Neural networks are traditionally trained under the assumption that data come from a stationary distribution. However, settings which violate this assumption are becoming more popular; examples include supervised learning under distributional shifts, reinforcement learning, continual learning and non-stationary contextual bandits. In this work we introduce a novel learning approach that automatically models and adapts to non-stationarity, via an Ornstein-Uhlenbeck process with an adaptive drift parameter. The adaptive drift tends to draw the parameters towards the initialisation distribution, so the approach can be understood as a form of soft parameter reset. We show empirically that our approach performs well in non-stationary supervised and off-policy reinforcement learning settings.
Revisiting Clustering of Neural Bandits: Selective Reinitialization for Mitigating Loss of Plasticity
Gaoling School of Artificial Intelligence Renmin University of China Beijing China；Gaoling School of Artificial Intelligence Renmin University of China
Adaptive Requesting in Decentralized Edge Networks via Non-Stationary Bandits
School of Information and Communication Engineering, University of Electronic Science and Technology of China（信息与通信工程学院，电子科学与技术大学）；School of Computer Science and Electronic Engineering, University of Essex（计算机科学与电子工程学院，埃塞克斯大学）；Department of Electrical and Computer Engineering, Rutgers University（电气与计算机工程系，罗格斯大学）
