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gatti christopher - design of experiments for reinforcement learning
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Design of Experiments for Reinforcement Learning




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Dettagli

Genere:Libro
Lingua: Inglese
Editore:

Springer

Pubblicazione: 09/2016
Edizione: Softcover reprint of the original 1st ed. 2015





Trama

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.




Sommario

Introduction.- Reinforcement Learning. Design of Experiments.- Methodology.- The Mountain Car Problem.- The Truck Backer-Upper Problem.- The Tandem Truck Backer-Upper Problem.- Appendices.




Autore

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.











Altre Informazioni

ISBN:

9783319385518

Condizione: Nuovo
Collana: Springer Theses
Dimensioni: 235 x 155 mm
Formato: Brossura
Illustration Notes:XIII, 191 p. 46 illus., 25 illus. in color.
Pagine Arabe: 191
Pagine Romane: xiii


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