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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings
Editors	Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Corchado Rodríguez
Publisher	Springer Verlag
Pages	758-769
Number of pages	12
ISBN (Print)	9783030298586
DOIs	https://doi.org/10.1007/978-3-030-29859-3_64
Publication status	Published - 2019
Externally published	Yes
Event	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 - León, Spain Duration: 4 Sept 2019 → 6 Sept 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11734 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019
Country/Territory	Spain
City	León
Period	4/09/19 → 6/09/19

Access to Document

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

Cite this

Fernandez-Gauna, B., Larrucea, X., & Graña, M. (2019). Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. In 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",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019 ; Conference date: 04-09-2019 Through 06-09-2019",

year = "2019",

doi = "10.1007/978-3-030-29859-3\_64",

language = "English",

isbn = "9783030298586",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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editor = "\{P{\'e}rez Garc{\'i}a\}, Hilde and \{S{\'a}nchez Gonz{\'a}lez\}, Lidia and \{Castej{\'o}n Limas\}, Manuel and \{Quinti{\'a}n Pardo\}, H{\'e}ctor and \{Corchado Rodr{\'i}guez\}, Emilio",

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Fernandez-Gauna, B, Larrucea, X & Graña, M 2019, Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. in 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, Spain, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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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U2 - 10.1007/978-3-030-29859-3_64

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

M3 - Conference contribution

AN - SCOPUS:85072893910

SN - 9783030298586

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Hybrid Artificial Intelligent Systems - 14th International Conference, HAIS 2019, Proceedings

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A2 - Sánchez González, Lidia

A2 - Castejón Limas, Manuel

A2 - Quintián Pardo, Héctor

A2 - Corchado Rodríguez, Emilio

PB - Springer Verlag

T2 - 14th International Conference on Hybrid Artificial Intelligence Systems, HAIS 2019

Y2 - 4 September 2019 through 6 September 2019

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Fernandez-Gauna B, Larrucea X, Graña M. Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms. In Pérez García H, Sánchez González L, Castejón Limas M, Quintián Pardo H, Corchado Rodríguez E, editors, 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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