Optimal Regret Bounds via Low-Rank Structured Variation in Non-Stationary Reinforcement Learning

We study reinforcement learning in non-stationary communicating MDPs whose transition drift admits a low-rank plus sparse structure. We propose \textbf{SVUCRL} (Structured Variation UCRL) and prove the dynamic-regret bound \[\scalebox{0.75}{$\displaystyle