arXiv stat.ML
· Papers
Non-Asymptotic Best Policy Identification Guarantees in Online Reinforcement Learning
arXiv:2607.17201v1 Announce Type: new Abstract: In this work we study the Best Policy Identification (BPI) problem in online, tabular Reinforcement Learning. This is an active sequential hypothesis testing problem in which the learner's objective is to identify an optimal policy in a Markov Decision Process (MDP) with