Exploration-Driven Reinforcement Learning for Avionic System Fault Detection (Experience Paper)

Author:

Le Tolguenec Paul-Antoine1ORCID,Rachelson Emmanuel1ORCID,Besse Yann2ORCID,Teichteil-Koenigsbuch Florent2ORCID,Schneider Nicolas2ORCID,Waeselynck Hélène3ORCID,Wilson Dennis1ORCID

Affiliation:

1. ISAE SUPAERO, Toulouse, France

2. Airbus, Toulouse, France

3. LAAS-CNRS, Toulouse, France

Publisher

ACM

Reference66 articles.

1. 2022. ODI RESUME. https://static.nhtsa.gov/odi/inv/2022/INOA-PE22002-4385.PDF Accessed: 23/03/2024

2. Yasasa Abeysirigoonawardena, Florian Shkurti, and Gregory Dudek. 2019. Generating adversarial driving scenarios in high-fidelity simulators. In 2019 International Conference on Robotics and Automation (ICRA). 8271–8277.

3. It Does Matter How You Normalise the Branch Distance in Search Based Software Testing

4. Aitor Arrieta, Shuai Wang, Goiuria Sagardui, and Leire Etxeberria. 2016. Test case prioritization of configurable cyber-physical systems with weight-based search algorithms. In Proceedings of the Genetic and Evolutionary Computation Conference 2016. 1053–1060.

5. Arthur Aubret Laetitia Matignon and Salima Hassas. 2019. A survey on intrinsic motivation in reinforcement learning. arXiv e-prints arXiv–1908.

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