Abstract : A helpful teacher can significantly improve the learning rate of a learning agent. Teaching algorithms have been formally studied within the field of Algorithmic Teaching. These give important insights into how a teacher can select the most informative examples while teaching a new concept. However the field has so far focused purely on classification tasks. In this paper we introduce a novel method for optimally teaching sequential decision tasks. We present an algorithm that automatically selects the set of most informative demonstrations and evaluate it on several navigation tasks. Next, we explore the idea of using this algorithm to produce instructions for humans on how to choose examples when teaching sequential decision tasks. We present a user study that demonstrates the utility of such instructions.
https://hal.inria.fr/hal-00755253
Contributeur : Manuel Lopes
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Soumis le : mardi 20 novembre 2012 - 17:39:25
Dernière modification le : jeudi 16 novembre 2017 - 17:12:03
Document(s) archivé(s) le : jeudi 21 février 2013 - 12:30:57
Maya Cakmak, Manuel Lopes. Algorithmic and Human Teaching of Sequential Decision Tasks. AAAI Conference on Artificial Intelligence (AAAI-12), Jul 2012, Toronto, Canada. 2012. 〈hal-00755253〉
