{"citation":{"chicago":"Shi, Junjie, and Kuan-Hsun Chen. “Shielded Reinforcement Learning for Fault-Tolerant Scheduling in Real-Time Systems.” <i>Real-Time Systems</i> 61, no. 2 (2025): 306–10. <a href=\"https://doi.org/10.1007/s11241-025-09441-z\">https://doi.org/10.1007/s11241-025-09441-z</a>.","short":"J. Shi, K.-H. Chen, Real-Time Systems 61 (2025) 306–310.","apa":"Shi, J., &#38; Chen, K.-H. (2025). Shielded reinforcement learning for fault-tolerant scheduling in real-time systems. <i>Real-Time Systems</i>, <i>61</i>(2), 306–310. <a href=\"https://doi.org/10.1007/s11241-025-09441-z\">https://doi.org/10.1007/s11241-025-09441-z</a>","ieee":"J. Shi and K.-H. Chen, “Shielded reinforcement learning for fault-tolerant scheduling in real-time systems,” <i>Real-Time Systems</i>, vol. 61, no. 2, pp. 306–310, 2025, doi: <a href=\"https://doi.org/10.1007/s11241-025-09441-z\">10.1007/s11241-025-09441-z</a>.","ama":"Shi J, Chen K-H. Shielded reinforcement learning for fault-tolerant scheduling in real-time systems. <i>Real-Time Systems</i>. 2025;61(2):306-310. doi:<a href=\"https://doi.org/10.1007/s11241-025-09441-z\">10.1007/s11241-025-09441-z</a>","bibtex":"@article{Shi_Chen_2025, title={Shielded reinforcement learning for fault-tolerant scheduling in real-time systems}, volume={61}, DOI={<a href=\"https://doi.org/10.1007/s11241-025-09441-z\">10.1007/s11241-025-09441-z</a>}, number={2}, journal={Real-Time Systems}, publisher={Springer Science and Business Media LLC}, author={Shi, Junjie and Chen, Kuan-Hsun}, year={2025}, pages={306–310} }","mla":"Shi, Junjie, and Kuan-Hsun Chen. “Shielded Reinforcement Learning for Fault-Tolerant Scheduling in Real-Time Systems.” <i>Real-Time Systems</i>, vol. 61, no. 2, Springer Science and Business Media LLC, 2025, pp. 306–10, doi:<a href=\"https://doi.org/10.1007/s11241-025-09441-z\">10.1007/s11241-025-09441-z</a>."},"status":"public","user_id":"128464","volume":61,"page":"306-310","_id":"66147","publisher":"Springer Science and Business Media LLC","abstract":[{"lang":"eng","text":"<jats:title>Abstract</jats:title>\n          <jats:p>Reinforcement Learning (RL) has emerged as a promising tool for decision-making in various applications, particularly in uncertain environments. While its adoption in embedded systems—especially hard real-time systems—faces challenges due to stringent timing constraints, integrating shielding mechanisms may offer a pathway for RL to optimize its scheduling decisions, preserving worst-case timing guarantees. This position paper shows a use case where RL selects compliant execution versions for fault-tolerant real-time systems while minimizing the system utilization in runtime. Furthermore, we discuss possible directions for further exploring RL’s role in real-time systems for improved adaptability.</jats:p>"}],"issue":"2","publication":"Real-Time Systems","type":"journal_article","date_created":"2026-07-03T21:07:18Z","date_updated":"2026-07-05T14:48:45Z","publication_status":"published","intvolume":"        61","title":"Shielded reinforcement learning for fault-tolerant scheduling in real-time systems","year":"2025","author":[{"last_name":"Shi","first_name":"Junjie","full_name":"Shi, Junjie"},{"last_name":"Chen","first_name":"Kuan-Hsun","full_name":"Chen, Kuan-Hsun"}],"publication_identifier":{"issn":["0922-6443","1573-1383"]},"doi":"10.1007/s11241-025-09441-z","language":[{"iso":"eng"}]}