Upper Confidence Trees and Billiards for Optimal Active Learning
Résumé
This paper focuses on Active Learning (AL) with bounded compu- tational resources. AL is formalized as a finite horizon Reinforcement Learning problem, and tackled as a single-player game. An approximate optimal AL strat- egy based on tree-structured multi-armed bandit algorithms and billiard-based sampling is presented together with a proof of principle of the approach.
Domaines
Optimisation et contrôle [math.OC]| Origine | Fichiers produits par l'(les) auteur(s) |
|---|
Loading...