Design of Experiments for Reinforcement Learning
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 com...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2015, 2015
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| Edition: | 1st ed. 2015 |
| Series: | Springer Theses, Recognizing Outstanding Ph.D. Research
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| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
| Summary: | 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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| Physical Description: | XIII, 191 p. 46 illus., 25 illus. in color online resource |
| ISBN: | 9783319121970 |