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Reinforcement Learning: Insights from Interesting Failures in Parameter Selection

  • Wolfgang Konen
  • Thomas Bartz–Beielstein
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5199)

Abstract

We investigate reinforcement learning methods, namely the temporal difference learning TD(λ) algorithm, on game-learning tasks. Small modifications in algorithm setup and parameter choice can have significant impact on success or failure to learn. We demonstrate that small differences in input features influence significantly the learning process. By selecting the right feature set we found good results within only 1/100 of the learning steps reported in the literature. Different metrics for measuring success in a reproducible manner are developed. We discuss why linear output functions are often preferable compared to sigmoid output functions.

Keywords

Hide Neuron Strategic Game Learning Agent Board Position Reinforcement Learning Agent 
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-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Wolfgang Konen
    • 1
  • Thomas Bartz–Beielstein
    • 1
  1. 1.Faculty for Computer Science and Engineering ScienceCologne University of Applied SciencesGummersbachGermany

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