Design of Experiments for Reinforcement Learning

  • Christopher Gatti

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Christopher Gatti
    Pages 1-5
  3. Christopher Gatti
    Pages 7-52
  4. Christopher Gatti
    Pages 53-66
  5. Christopher Gatti
    Pages 67-93
  6. Christopher Gatti
    Pages 95-109
  7. Christopher Gatti
    Pages 111-127
  8. Christopher Gatti
    Pages 129-139
  9. Christopher Gatti
    Pages 141-156
  10. Back Matter
    Pages 157-191

About this book

Introduction

This thesis takes an empirical approach to understanding of the behavior and interactions between the two main components of reinforcement learning: the learning algorithm and the functional representation of learned knowledge. The author approaches these entities using design of experiments not commonly employed to study machine learning methods. The results outlined in this work provide insight as to what enables and what has an effect on successful reinforcement learning implementations so that this learning method can be applied to more challenging problems.

Keywords

Kriging Covariance Functions Reinforcement Learning Algorithm Response Surface Metamodeling Sequential CART Stochastic Kriging

Authors and affiliations

  • Christopher Gatti
    • 1
  1. 1.Industrial and Systems EngineeringRensselaer Polytechnic InstituteTroyUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-12197-0
  • Copyright Information Springer International Publishing Switzerland 2015
  • Publisher Name Springer, Cham
  • eBook Packages Engineering
  • Print ISBN 978-3-319-12196-3
  • Online ISBN 978-3-319-12197-0
  • Series Print ISSN 2190-5053
  • Series Online ISSN 2190-5061
  • About this book
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