Design of Experiments for Reinforcement Learning

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Design of Experiments for Reinforcement Learning

Christopher Gatti - Personal Name;

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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Series Title: -
Call Number: 006.3
Publisher: Cham : Springer., 2015
Collation: 196 pages
Language: English
ISBN/ISSN: 9783319121970
Classification: 006.3
Content Type: -
Media Type: -
Carrier Type: online resource
Edition: -
Subject(s): Artificial Intelligence
Specific Detail Info: -
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Cataloger: Kurnadi
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