Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms

Borja Fernandez-Gauna; Xabier Larrucea; Manuel Graña

doi:10.1007/978-3-030-29859-3_64

Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms

Borja Fernandez-Gauna^*
, Xabier Larrucea
, Manuel Graña

^*Autor correspondiente de este trabajo

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Researchers working with Reinforcement Learning typically face issues that severely hinder the efficiency of their research workflow. These issues include high computational requirements, numerous hyper-parameters that must be set manually, and the high probability of failing a lot of times before success. In this paper, we present some of the challenges our research has faced and the way we have tackled successfully them in an innovative software platform. We provide some benchmarking results that show the improvements introduced by the new platform.

Idioma original	Inglés
Título de la publicación alojada	Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings
Editores	Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Corchado Rodríguez
Editorial	Springer Verlag
Páginas	758-769
Número de páginas	12
ISBN (versión impresa)	9783030298586
DOI	https://doi.org/10.1007/978-3-030-29859-3_64
Estado	Publicada - 2019
Publicado de forma externa	Sí
Evento	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 - León, Espana Duración: 4 sept 2019 → 6 sept 2019

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	11734 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019
País/Territorio	Espana
Ciudad	León
Período	4/09/19 → 6/09/19

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10.1007/978-3-030-29859-3_64

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Fernandez-Gauna, B., Larrucea, X., & Graña, M. (2019). Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. En H. Pérez García, L. Sánchez González, M. Castejón Limas, H. Quintián Pardo, & E. Corchado Rodríguez (Eds.), Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings (pp. 758-769). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11734 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-29859-3_64

Fernandez-Gauna, Borja ; Larrucea, Xabier ; Graña, Manuel. / Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. editor / Hilde Pérez García ; Lidia Sánchez González ; Manuel Castejón Limas ; Héctor Quintián Pardo ; Emilio Corchado Rodríguez. Springer Verlag, 2019. pp. 758-769 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms",

abstract = "Researchers working with Reinforcement Learning typically face issues that severely hinder the efficiency of their research workflow. These issues include high computational requirements, numerous hyper-parameters that must be set manually, and the high probability of failing a lot of times before success. In this paper, we present some of the challenges our research has faced and the way we have tackled successfully them in an innovative software platform. We provide some benchmarking results that show the improvements introduced by the new platform.",

author = "Borja Fernandez-Gauna and Xabier Larrucea and Manuel Gra{\~n}a",

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year = "2019",

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Fernandez-Gauna, B, Larrucea, X & Graña, M 2019, Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. En H Pérez García, L Sánchez González, M Castejón Limas, H Quintián Pardo & E Corchado Rodríguez (eds.), Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11734 LNAI, Springer Verlag, pp. 758-769, 14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019, León, Espana, 4/09/19. https://doi.org/10.1007/978-3-030-29859-3_64

Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. / Fernandez-Gauna, Borja; Larrucea, Xabier; Graña, Manuel.
Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. ed. / Hilde Pérez García; Lidia Sánchez González; Manuel Castejón Limas; Héctor Quintián Pardo; Emilio Corchado Rodríguez. Springer Verlag, 2019. p. 758-769 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11734 LNAI).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Larrucea, Xabier

AU - Graña, Manuel

PY - 2019

Y1 - 2019

N2 - Researchers working with Reinforcement Learning typically face issues that severely hinder the efficiency of their research workflow. These issues include high computational requirements, numerous hyper-parameters that must be set manually, and the high probability of failing a lot of times before success. In this paper, we present some of the challenges our research has faced and the way we have tackled successfully them in an innovative software platform. We provide some benchmarking results that show the improvements introduced by the new platform.

AB - Researchers working with Reinforcement Learning typically face issues that severely hinder the efficiency of their research workflow. These issues include high computational requirements, numerous hyper-parameters that must be set manually, and the high probability of failing a lot of times before success. In this paper, we present some of the challenges our research has faced and the way we have tackled successfully them in an innovative software platform. We provide some benchmarking results that show the improvements introduced by the new platform.

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Fernandez-Gauna B, Larrucea X, Graña M. Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. En Pérez García H, Sánchez González L, Castejón Limas M, Quintián Pardo H, Corchado Rodríguez E, editores, Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings. Springer Verlag. 2019. p. 758-769. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-29859-3_64

Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms

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