Tag: grained

Q-Learning with Fine-Grained Gap-Dependent Regret arXiv:2510.06647v1 Announce Type: new Abstract: We study fine-grained gap-dependent regret bounds for model-free reinforcement learning in episodic tabular Markov Decision Processes. Existing model-free algorithms achieve minimax worst-case regret, but their gap-dependent bounds remain coarse and fail to fully capture the structure of suboptimality gaps. We address this limitation by establishing…