Q-Learning with Fine-Grained Gap-Dependent Regret
Researchers have developed new algorithms for Q-learning that provide more precise regret bounds in episodic tabular Markov Decision Processes. These advancements address limitations in existing methods by offering fine-grained, gap-dependent regret guarantees. The study introduces a novel analytical framework and proposes new algorithms, ULCB-Hoeffding and a refined AMB, which demonstrate improved performance and theoretical rigor. AI