Intrinsically motivated exploration as efficient active learning in unknown and unprepared spaces

Pierre-Yves Oudeyer; Adrien Baranes

Intrinsically motivated exploration as efficient active learning in unknown and unprepared spaces

Pierre-Yves Oudeyer ¹ Adrien Baranes ¹

1 Flowers - Flowing Epigenetic Robots and Systems

Inria Bordeaux - Sud-Ouest, U2IS - Unité d'Informatique et d'Ingénierie des Systèmes

Abstract : Intrinsic motivations are mechanisms that guide curiosity-driven exploration (Berlyne, 1965). They have been proposed to be crucial for self-organizing developmental trajectories (Oudeyer et al. , 2007) as well as for guiding the learning of general and reusable skills (Barto et al., 2005). Here, we argue that they can be considered as “active learning” algorithms, and show that some of them also allow for very efficient learning in unprepared sensorimotor spaces, outperforming existing active learning algorithms.

Type de document :

Communication dans un congrès

the 8th International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, 2008, Brighton, United Kingdom. Lund University Cognitive Studies, Lund:LUCS, Brighton., 2p., 2008

Domaine :

Informatique [cs] / Robotique [cs.RO]

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https://hal.inria.fr/inria-00420125
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OudeyerBaranesEpirob08.pdf

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Intrinsically motivated exploration as efficient active learning in unknown and unprepared spaces

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