Analyse neuronaler Daten

Autonomous Learning of State Representations for Control: An Emerging Field Aims to Autonomously Learn State Representations for Reinforcement Learning Agents from Their Real-World Sensor Observations
Zitatschlüssel	Boehmer2015c
Autor	Böhmer, W. and Springenberg, J. T. and Boedecker, J. and Riedmiller, M. and Obermayer, K.
Buchtitel	Künstliche Intelligenz
Seiten	353-362
Jahr	2015
ISSN	0933-1875, 1610-1987
DOI	10.1007/s13218-015-0356-1
Jahrgang	29
Nummer	4
Verlag	Springer Berlin Heidelberg
Serie	Technical Contribution
Zusammenfassung	This article reviews an emerging field that aims for autonomous reinforcement learning (RL) directly on sensor-observations. Straightforward end-to-end RL has recently shown remarkable success, but relies on large amounts of samples. As this is not feasible in robotics, we review two approaches to learn intermediate state representations from previous experiences: deep auto-encoders and slow-feature analysis. We analyze theoretical properties of the representations and point to potential improvements.

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