Reinforcement Learning Experiments Running Efficiently over Widly Heterogeneous Computer Farms

Borja Fernandez-Gauna ^[1] ; Xabier Larrucea ^[1] ; Manuel Graña ^[1]
1. [1] University of the Basque Country, UPV/EHU. Computational Intelligence Group (Leioa, Vizcaya)
Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García , Lidia Sánchez González , Manuel Castejón Limas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2019, ISBN 978-3-030-29858-6, págs. 758-769
Idioma: inglés
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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 hyperparameters 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.