Design of Experiments for Reinforcement Learning (Hardcover, 2015 ed.)

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.

General

Imprint:	Springer International Publishing AG
Country of origin:	Switzerland
Series:	Springer Theses
Release date:	November 2014
First published:	2015
Authors:	Christopher Gatti
Dimensions:	235 x 155 x 13mm (L x W x T)
Format:	Hardcover
Pages:	191
Edition:	2015 ed.
ISBN-13:	978-3-319-12196-3
Categories:	Books > Computing & IT > Computer hardware & operating systems > Computer architecture & logic design > General Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	3-319-12196-0
Barcode:	9783319121963

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