Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems
<|reference_start|>Tolerance of Reinforcement Learning Controllers against Deviations in Cyber Physical Systems: 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.<|reference_end|>
@article{zhang2024tolerance,
    title={Tolerance of Reinforcement Learning Controllers against Deviations in
  Cyber Physical Systems},
    author={Changjian Zhang, Parv Kapoor, Eunsuk Kang, Romulo Meira-Goes, David
  Garlan, Akila Ganlath, Shatadal Mishra, Nejib Ammar},
    journal={arXiv preprint arXiv:2406.17066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17066},
    primaryClass={eess.SY cs.AI cs.LO cs.RO cs.SY}
}