@article{66147,
  abstract     = {{<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>}},
  author       = {{Shi, Junjie and Chen, Kuan-Hsun}},
  issn         = {{0922-6443}},
  journal      = {{Real-Time Systems}},
  number       = {{2}},
  pages        = {{306--310}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Shielded reinforcement learning for fault-tolerant scheduling in real-time systems}}},
  doi          = {{10.1007/s11241-025-09441-z}},
  volume       = {{61}},
  year         = {{2025}},
}