Lectures in Active Sequential Hypothesis Testing and Adaptive Exploration in Reinforcement Learning - Lecture 3

Lecture 3: Stopping rules and design of optimal algorithms This lecture focuses on the design of optimal algorithms for the BAI problem that attain the sample complexity lower bound. We will see how to derive anytime confidence intervals, and design algorithms that are asymptotically optimal in the confidence parameter. We will derive guarantees that hold in high probability, and in expectation. Lecture notes will be provided in advance.