Design of experiments for reinforcement learning [electronic resource] / Christopher Gatti.

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Design of experiments for reinforcement learning [electronic resource] / Christopher Gatti.

9783319121963
9783319121970 electronic book
3319121979 electronic book

Cham, Switzerland : Springer, 2015.

©2015

English

1 online resource (196 pages) : illustrations.

10.1007/978-3-319-12197-0 doi

Q325.6 .G388 2015eb

006.31

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.

Includes bibliographical references.

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