2009
Rolet, Philippe
Sebag, Michèle
Teytaud, Olivier

Abstract: 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.

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