---
_id: '66147'
abstract:
- lang: eng
  text: |-
    <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:
- first_name: Junjie
  full_name: Shi, Junjie
  last_name: Shi
- first_name: Kuan-Hsun
  full_name: Chen, Kuan-Hsun
  last_name: Chen
citation:
  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>
  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>
  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}
    }'
  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>.'
  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>.'
  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>.
  short: J. Shi, K.-H. Chen, Real-Time Systems 61 (2025) 306–310.
date_created: 2026-07-03T21:07:18Z
date_updated: 2026-07-05T14:48:45Z
doi: 10.1007/s11241-025-09441-z
intvolume: '        61'
issue: '2'
language:
- iso: eng
page: 306-310
publication: Real-Time Systems
publication_identifier:
  issn:
  - 0922-6443
  - 1573-1383
publication_status: published
publisher: Springer Science and Business Media LLC
status: public
title: Shielded reinforcement learning for fault-tolerant scheduling in real-time
  systems
type: journal_article
user_id: '128464'
volume: 61
year: '2025'
...