Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916299273142272 |
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| author | Zhang, Changjian Kapoor, Parv Kang, Eunsuk Meira-Goes, Romulo Garlan, David Ganlath, Akila Mishra, Shatadal Ammar, Nejib |
| author_facet | Zhang, Changjian Kapoor, Parv Kang, Eunsuk Meira-Goes, Romulo Garlan, David Ganlath, Akila Mishra, Shatadal Ammar, Nejib |
| contents | Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things(IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbancesin the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17066 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems Zhang, Changjian Kapoor, Parv Kang, Eunsuk Meira-Goes, Romulo Garlan, David Ganlath, Akila Mishra, Shatadal Ammar, Nejib Systems and Control Artificial Intelligence Logic in Computer Science Robotics Cyber-physical systems (CPS) with reinforcement learning (RL)-based controllers are increasingly being deployed in complex physical environments such as autonomous vehicles, the Internet-of-Things(IoT), and smart cities. An important property of a CPS is tolerance; i.e., its ability to function safely under possible disturbances and uncertainties in the actual operation. In this paper, we introduce a new, expressive notion of tolerance that describes how well a controller is capable of satisfying a desired system requirement, specified using Signal Temporal Logic (STL), under possible deviations in the system. Based on this definition, we propose a novel analysis problem, called the tolerance falsification problem, which involves finding small deviations that result in a violation of the given requirement. We present a novel, two-layer simulation-based analysis framework and a novel search heuristic for finding small tolerance violations. To evaluate our approach, we construct a set of benchmark problems where system parameters can be configured to represent different types of uncertainties and disturbancesin the system. Our evaluation shows that our falsification approach and heuristic can effectively find small tolerance violations. |
| title | Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems |
| topic | Systems and Control Artificial Intelligence Logic in Computer Science Robotics |
| url | https://arxiv.org/abs/2406.17066 |