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RLPR – An Aspectualizable State Space Representation

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Qualitative Spatial Abstraction in Reinforcement Learning

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Abstract

In this chapter, we get to know a qualitative spatial representation tailored for indoor robot navigation tasks. First, the task to be solved is introduced and task space and structure space are identified along with considerations about frames of reference (Sect. 6.1). In Sect. 6.2 we derive a suitable representation for task space based on the relative positions of landmarks.

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Authors and Affiliations

  1. Cognitive Systems Group, Department of Mathematics and Informatics, University of Bremen, P.O. Box 330 440, 28334, Bremen, Germany

    Dr.-Ing. Lutz Frommberger

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  1. Dr.-Ing. Lutz Frommberger
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Correspondence to Lutz Frommberger .

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Frommberger, L. (2010). RLPR – An Aspectualizable State Space Representation. In: Qualitative Spatial Abstraction in Reinforcement Learning. Cognitive Technologies. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16590-0_6

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  • DOI: https://doi.org/10.1007/978-3-642-16590-0_6

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  • Print ISBN: 978-3-642-16589-4

  • Online ISBN: 978-3-642-16590-0

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