Qualitative Spatial Abstraction in Reinforcement Learning

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to...

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Main Author: Frommberger, Lutz
Corporate Author: SpringerLink (Online service)
Format: eBook
Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010, 2010
Edition:1st ed. 2010
Series:Cognitive Technologies
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Table of Contents:
  • Foundations of Reinforcement Learning
  • Abstraction and Knowledge Transfer in Reinforcement Learning
  • Qualitative State Space Abstraction
  • Generalization and Transfer Learning with Qualitative Spatial Abstraction
  • RLPR – An Aspectualizable State Space Representation
  • Empirical Evaluation
  • Summary and Outlook