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Design of experiments for reinforcem...

Gatti, Christopher.

Design of experiments for reinforcement learning[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.31
書名/作者:	Design of experiments for reinforcement learning/ by Christopher Gatti.
作者:	Gatti, Christopher.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xiii, 191 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Artificial intelligence.
標題:	Engineering.
標題:	Computational Intelligence.
標題:	Logic Design.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Reinforcement learning.
ISBN:	9783319121970 (electronic bk.)
ISBN:	9783319121963 (paper)
內容註:	Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices.
摘要、提要註:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-12197-0

Design of experiments for reinforcement learning[electronic resource] /
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:

172060
Artificial intelligence.

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

Dewey Class. No.: 006.31

Design of experiments for reinforcement learning[electronic resource] /
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