Jing-Yuan Liu

Bellman-Centric Learning: Near-Optimal Regret for Linear Bandits with Memory

We study linear bandits with memory, where past actions induce endogenous nonstationarity through an arbitrary known, bounded matrix-valued memory map. To trade off exploration and exploitation while accounting for the memory dynamics, we develop RSM-LinUCB, a Bellman-centric algorithm that learns as in linear bandits...