A Methodology to Build Decision Analysis Tools Applied to Distributed Reinforcement Learning
In: ScaDL 2022 - Scalable Deep Learning over Parallel and Distributed Infrastructures - An IPDPS Workshop ; https://hal.science/hal-03613558 ; ScaDL 2022 - Scalable Deep Learning over Parallel and Distributed Infrastructures - An IPDPS Workshop, Jun 2022, Lyon / Virtual, France. pp.1-10, 2022
Online
Konferenz
Zugriff:
International audience ; As Artificial Intelligence-based applications become more and more complex, speeding up the learning phase (which is typically computation-intensive) becomes more and more necessary. Distributed machine learning (ML) appears adequate to address this problem. Unfortunately, ML also brings new development frameworks, methodologies and high-level programming languages that do not fit to the regular high-performance computing design flow. This paper introduces a methodology to build a decision making tool that allows ML experts to arbitrate between different frameworks and deployment configurations, in order to fulfill project objectives such as the accuracy of the resulting model, the computing speed or the energy consumption of the learning computation. The proposed methodology is applied to an industrial-grade case study in which reinforcement learning is used to train an autonomous steering model for a cargo airdrop system. Results are presented within a Pareto front that lets ML experts choose an appropriate solution, a framework and a deployment configuration, based on the current operational situation. While the proposed approach can effortlessly be applied to other machine learning problems, as for many decision making systems, the selected solutions involve a trade-off between several antagonist evaluation criteria and require experts from different domains to pick the most efficient solution from the short list. Nevertheless, this methodology speeds up the development process by clearly discarding, or, on the contrary, including combinations of frameworks and configurations, which has a significant impact for time and budget-constrained projects.
Titel: |
A Methodology to Build Decision Analysis Tools Applied to Distributed Reinforcement Learning
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Autor/in / Beteiligte Person: | Prigent, Cédric ; Cudennec, Loïc ; Costan, Alexandru ; Antoniu, Gabriel ; Scalable Storage for Clouds and Beyond (KerData) ; Inria Rennes – Bretagne Atlantique ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SYSTÈMES LARGE ÉCHELLE (IRISA-D1) ; Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) ; Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique) ; Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes) ; Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA) ; Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique) ; Institut Mines-Télécom Paris (IMT)-Institut Mines-Télécom Paris (IMT) ; DGA Maîtrise de l'information (DGA.MI) ; Direction générale de l'Armement (DGA) |
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Zeitschrift: | ScaDL 2022 - Scalable Deep Learning over Parallel and Distributed Infrastructures - An IPDPS Workshop ; https://hal.science/hal-03613558 ; ScaDL 2022 - Scalable Deep Learning over Parallel and Distributed Infrastructures - An IPDPS Workshop, Jun 2022, Lyon / Virtual, France. pp.1-10, 2022 |
Veröffentlichung: | HAL CCSD, 2022 |
Medientyp: | Konferenz |
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