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Adaptive Sparse Grids in Reinforcement Learning

 
: Garcke, Jochen; Klompmaker, Irene

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Volltext (PDF; )

Dahlke, S.; Dahmen, W.; Griebel, M.; Hackbusch, W.; Ritter, K.; Schneider, R.; Schwab, C.; Yserentant, H.:
Extraction of Quantifiable Information from Complex Systems
Cham: Springer International Publishing, 2014 (Lecture notes in computational science and engineering 102)
ISBN: 978-3-319-08158-8 (Print)
ISBN: 978-3-319-08159-5 (Online)
S.179-194
Englisch
Aufsatz in Buch, Elektronische Publikation
Fraunhofer SCAI ()

Abstract
We propose a model-based online reinforcement learning approach for continuous domains with deterministic transitions using a spatially adaptive sparse grid in the planning stage. The model learning employs Gaussian processes regression and allows a low sample complexity. The adaptive sparse grid is introduced to allow the representation of the value function in the planning stage in higher dimensional state spaces. This work gives numerical evidence that adaptive sparse grids are applicable in the case of reinforcement learning.

: http://publica.fraunhofer.de/dokumente/N-326422.html