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Design of Experiments for Reinforcement Learning

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Design of Experiments for Reinforcement Learning Synopsis

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.

About This Edition

ISBN: 9783319385518
Publication date:
Author: Christopher Gatti
Publisher: Springer an imprint of Springer International Publishing
Format: Paperback
Pagination: 191 pages
Series: Springer Theses
Genres: Artificial intelligence
Computer architecture and logic design