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Design of experiments for reinforcem...

Gatti, Christopher.

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Design of experiments for reinforcement learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Design of experiments for reinforcement learning/ by Christopher Gatti.
Author:	Gatti, Christopher.
Published:	Cham :Springer International Publishing : : 2015.,
Description:	xiii, 191 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
Contained By:	Springer eBooks
Subject:	Reinforcement learning. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-12197-0
ISBN:	9783319121970 (electronic bk.)

Design of experiments for reinforcement learning
Gatti, Christopher.

Design of experiments for reinforcement learning[electronic resource] /by Christopher Gatti. - Cham :Springer International Publishing :2015. - xiii, 191 p. :ill., digital ;24 cm. - Springer theses,2190-5053. - Springer theses..

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

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.

ISBN: 9783319121970 (electronic bk.)

Standard No.: 10.1007/978-3-319-12197-0doiSubjects--Topical Terms:

1006373
Reinforcement learning.

LC Class. No.: Q325.6 / .G388 2015

Dewey Class. No.: 006.31

Design of experiments for reinforcement learning
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245 1 0 $a Design of experiments for reinforcement learning $h [electronic resource] / $c by Christopher Gatti.
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520 $a 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.
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650 0 $a Artificial intelligence. $3 516317
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