Shielded reinforcement learning for fault-tolerant scheduling in real-time systems Shi, Junjie Chen, Kuan-Hsun <jats:title>Abstract</jats:title> <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> Springer Science and Business Media LLC 2025 info:eu-repo/semantics/article doc-type:article text http://purl.org/coar/resource_type/c_6501 https://ris.uni-paderborn.de/record/66147 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> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/s11241-025-09441-z info:eu-repo/semantics/altIdentifier/issn/0922-6443 info:eu-repo/semantics/altIdentifier/issn/1573-1383 info:eu-repo/semantics/closedAccess