Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning
In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime o...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of Control Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/11010134/ |
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| author | Josh Netter Kyriakos G. Vamvoudakis Timothy F. Walsh Jaideep Ray |
| author_facet | Josh Netter Kyriakos G. Vamvoudakis Timothy F. Walsh Jaideep Ray |
| author_sort | Josh Netter |
| collection | DOAJ |
| description | In this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model. |
| format | Article |
| id | doaj-art-e72589d3a6eb4f20b97f92c96d460e38 |
| institution | Kabale University |
| issn | 2694-085X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Control Systems |
| spelling | doaj-art-e72589d3a6eb4f20b97f92c96d460e382025-08-20T03:27:14ZengIEEEIEEE Open Journal of Control Systems2694-085X2025-01-01417218610.1109/OJCSYS.2025.357237511010134Safe and Robust Binary Classification and Fault Detection Using Reinforcement LearningJosh Netter0https://orcid.org/0009-0001-0566-9024Kyriakos G. Vamvoudakis1https://orcid.org/0000-0003-1978-4848Timothy F. Walsh2https://orcid.org/0000-0002-9527-4025Jaideep Ray3https://orcid.org/0009-0000-9908-7035Daniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USADaniel Guggenheim School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USASandia National Laboratories, Albuquerque, NM, USASandia National Laboratories, Albuquerque, NM, USAIn this paper, we propose a learning-based method utilizing the Soft Actor-Critic (SAC) algorithm to train a binary Support Vector Machine (SVM) classifier. This classifier is designed to identify valid input spaces in high-dimensional, highly constrained systems while minimizing the total runtime of offline simulations. The simulations adapt their runtime based on the likelihood that a given training input will be informative to the classifier. Furthermore, we introduce a method for using the trained SAC model to predict whether a desired system input is likely to violate constraints, along with a technique to adjust the input as necessary. Additionally, we explore the potential of this model to detect faults or adversarial attacks within the system. The effectiveness of our approach is demonstrated through various simulations of challenging classification problems and a constrained quadrotor model.https://ieeexplore.ieee.org/document/11010134/Machine learningreinforcement learningrobust control |
| spellingShingle | Josh Netter Kyriakos G. Vamvoudakis Timothy F. Walsh Jaideep Ray Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning IEEE Open Journal of Control Systems Machine learning reinforcement learning robust control |
| title | Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning |
| title_full | Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning |
| title_fullStr | Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning |
| title_full_unstemmed | Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning |
| title_short | Safe and Robust Binary Classification and Fault Detection Using Reinforcement Learning |
| title_sort | safe and robust binary classification and fault detection using reinforcement learning |
| topic | Machine learning reinforcement learning robust control |
| url | https://ieeexplore.ieee.org/document/11010134/ |
| work_keys_str_mv | AT joshnetter safeandrobustbinaryclassificationandfaultdetectionusingreinforcementlearning AT kyriakosgvamvoudakis safeandrobustbinaryclassificationandfaultdetectionusingreinforcementlearning AT timothyfwalsh safeandrobustbinaryclassificationandfaultdetectionusingreinforcementlearning AT jaideepray safeandrobustbinaryclassificationandfaultdetectionusingreinforcementlearning |