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Algebraic Reinforcement Learning

Hypothesis Induction for Relational Reinforcement Learning Using Term Generalization
  • Stefanie Neubert
  • Lenz BelznerEmail author
  • Martin Wirsing
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9200)

Abstract

The TG relational reinforcement learning algorithm builds first-order decision trees from perception samples. To this end, it statistically checks the significance of hypotheses about state properties possibly relevant for decision making. The generation of hypotheses is restricted by constraints manually specified a priori. In this paper we propose Algebraic Reinforcement Learning (ARL) for eliminating this condition by employing rewrite theories for state representation, enabling induction of hypotheses from perception samples directly via term generalization with the ACUOS system. We compare experimental results for ARL with and without generalization, and show that generalization positively influences convergence rates and reduces complexity of learned trees in comparison to trees learned without generalization.

Keywords

Regression Tree Domain Object Split Test Sort Order Language Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Stefanie Neubert
    • 1
  • Lenz Belzner
    • 1
    Email author
  • Martin Wirsing
    • 1
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany

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