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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Cham :
Springer International Publishing : Imprint: Springer,
2015.
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| Edición: | 1st ed. 2015. |
| Colección: | Springer Theses, Recognizing Outstanding Ph.D. Research,
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| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 008 | 141122s2015 sz | s |||| 0|eng d | ||
| 020 | |a 9783319121970 |9 978-3-319-12197-0 | ||
| 024 | 7 | |a 10.1007/978-3-319-12197-0 |2 doi | |
| 050 | 4 | |a Q342 | |
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| 072 | 7 | |a UYQ |2 thema | |
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| 100 | 1 | |a Gatti, Christopher. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Design of Experiments for Reinforcement Learning |h [electronic resource] / |c by Christopher Gatti. |
| 250 | |a 1st ed. 2015. | ||
| 264 | 1 | |a Cham : |b Springer International Publishing : |b Imprint: Springer, |c 2015. | |
| 300 | |a XIII, 191 p. 46 illus., 25 illus. in color. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 | |
| 505 | 0 | |a Introduction -- Reinforcement Learning. Design of Experiments -- Methodology -- The Mountain Car Problem -- The Truck Backer-Upper Problem -- The Tandem Truck Backer-Upper Problem -- Appendices. | |
| 520 | |a 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. | ||
| 650 | 0 | |a Computational intelligence. | |
| 650 | 0 | |a Logic design. | |
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 1 | 4 | |a Computational Intelligence. |
| 650 | 2 | 4 | |a Logic Design. |
| 650 | 2 | 4 | |a Artificial Intelligence. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9783319121987 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319121963 |
| 776 | 0 | 8 | |i Printed edition: |z 9783319385518 |
| 830 | 0 | |a Springer Theses, Recognizing Outstanding Ph.D. Research, |x 2190-5061 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-3-319-12197-0 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||