Upper Confidence Trees and Billiards for Optimal Active Learning
Abstract
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.
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