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  • Book
  • © 2015

Design of Experiments for Reinforcement Learning

  • Nominated by the Rensselaer Polytechnic Institute as an outstanding Ph.D. thesis

  • Explains reinforcement learning through a range of problems by exploring what affects reinforcement learning and what contributes to a successful implementation

  • Includes a contemporary design of experiments methods, comprising of a novel sequential experimentation procedure that finds convergent learning algorithm parameter subregions and stochastic kriging for response surface metamodeling

  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Theses (Springer Theses)

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-12197-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 139.99
Price excludes VAT (USA)
Hardcover Book USD 179.99
Price excludes VAT (USA)

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xiii
  2. Introduction

    • Christopher Gatti
    Pages 1-5
  3. Reinforcement Learning

    • Christopher Gatti
    Pages 7-52
  4. Design of Experiments

    • Christopher Gatti
    Pages 53-66
  5. Methodology

    • Christopher Gatti
    Pages 67-93
  6. The Mountain Car Problem

    • Christopher Gatti
    Pages 95-109
  7. The Truck Backer-upper Problem

    • Christopher Gatti
    Pages 111-127
  8. The Tandem Truck Backer-Upper Problem

    • Christopher Gatti
    Pages 129-139
  9. Discussion

    • Christopher Gatti
    Pages 141-156
  10. Back Matter

    Pages 157-191

About this book

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

  • Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, USA

    Christopher Gatti

About the author

Christopher Gatti received his PhD in Decision Sciences and Engineering Systems from Rensselaer Polytechnic Institute (RPI). During his time at RPI, his work focused on machine learning and statistics, with applications in reinforcement learning, graph search, stem cell RNA analysis, and neuro-electrophysiological signal analysis. Prior to beginning his graduate work at RPI, he received a BSE in mechanical engineering and an MSE in biomedical engineering, both from the University of Michigan. He then continued to work at the University of Michigan for three years doing computational biomechanics focusing on the shoulder and knee. He has been a gymnast since he was a child and is currently an acrobat for Cirque du Soleil.

Bibliographic Information

  • Book Title: Design of Experiments for Reinforcement Learning

  • Authors: Christopher Gatti

  • Series Title: Springer Theses

  • DOI: https://doi.org/10.1007/978-3-319-12197-0

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2015

  • Hardcover ISBN: 978-3-319-12196-3

  • Softcover ISBN: 978-3-319-38551-8

  • eBook ISBN: 978-3-319-12197-0

  • Series ISSN: 2190-5053

  • Series E-ISSN: 2190-5061

  • Edition Number: 1

  • Number of Pages: XIII, 191

  • Number of Illustrations: 21 b/w illustrations, 25 illustrations in colour

  • Topics: Computational Intelligence, Logic Design, Artificial Intelligence

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-12197-0
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book USD 139.99
Price excludes VAT (USA)
Hardcover Book USD 179.99
Price excludes VAT (USA)