Reliability assessment of off-policy deep reinforcement learning: A benchmark for aerodynamics

Reliability assessment of off-policy deep reinforcement learning: A benchmark for aerodynamics

Deep reinforcement learning (DRL) is promising for solving control problems in fluid mechanics, but it is a new field with many open questions. Possibilities are numerous and guidelines are rare concerning the choice of algorithms or best formulations for a given problem. Besides, DRL algorithms lea...

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Bibliographic Details
Main Authors: Sandrine Berger, Andrea Arroyo Ramo, Valentin Guillet, Thibault Lahire, Brice Martin, Thierry Jardin, Emmanuel Rachelson, Michaël Bauerheim
Format: Article
Language:English
Published: Cambridge University Press 2024-01-01
Series:Data-Centric Engineering
Subjects:
Benchmark for aerodynamics
computational fluid dynamics
deep reinforcement learning
off-policy algorithms
reliability
Online Access:https://www.cambridge.org/core/product/identifier/S263267362300028X/type/journal_article

Internet

https://www.cambridge.org/core/product/identifier/S263267362300028X/type/journal_article

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