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.
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Notes
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We use the terms experiment and experimental unit to distinguish two different concepts. The former refers to a configuration containing multi-valued hyper-parameters that will require several executions to finish, whereas the latter reefers to each of the single-valued configuration instances produced by combining the values of an experiment.
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Exp-B requires Microsoft’s Cognitive Library, which only runs on x64 platforms. That’s the reason Exp-B cannot run on Windows-x32 machines.
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Acknowledgements
The work in this paper has been partially supported by FEDER funds for the MINECO project TIN2017-85827-P, and projects KK-2018/00071 and KK-2018/00082 of the Elkartek 2018 funding program of the Basque Government.
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Fernandez-Gauna, B., Larrucea, X., Graña, M. (2019). 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. (eds) Hybrid Artificial Intelligent Systems. HAIS 2019. Lecture Notes in Computer Science(), vol 11734. Springer, Cham. https://doi.org/10.1007/978-3-030-29859-3_64
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