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<titleInfo><title>Shielded reinforcement learning for fault-tolerant scheduling in real-time systems</title></titleInfo>


<note type="publicationStatus">published</note>



<name type="personal">
  <namePart type="given">Junjie</namePart>
  <namePart type="family">Shi</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Kuan-Hsun</namePart>
  <namePart type="family">Chen</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>














<abstract lang="eng">&lt;jats:title&gt;Abstract&lt;/jats:title&gt;
          &lt;jats:p&gt;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.&lt;/jats:p&gt;</abstract>

<originInfo><publisher>Springer Science and Business Media LLC</publisher><dateIssued encoding="w3cdtf">2025</dateIssued>
</originInfo>
<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>Real-Time Systems</title></titleInfo>
  <identifier type="issn">0922-6443</identifier>
  <identifier type="issn">1573-1383</identifier><identifier type="doi">10.1007/s11241-025-09441-z</identifier>
<part><detail type="volume"><number>61</number></detail><detail type="issue"><number>2</number></detail><extent unit="pages">306-310</extent>
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<bibliographicCitation>
<apa>Shi, J., &amp;#38; Chen, K.-H. (2025). Shielded reinforcement learning for fault-tolerant scheduling in real-time systems. &lt;i&gt;Real-Time Systems&lt;/i&gt;, &lt;i&gt;61&lt;/i&gt;(2), 306–310. &lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;https://doi.org/10.1007/s11241-025-09441-z&lt;/a&gt;</apa>
<ieee>J. Shi and K.-H. Chen, “Shielded reinforcement learning for fault-tolerant scheduling in real-time systems,” &lt;i&gt;Real-Time Systems&lt;/i&gt;, vol. 61, no. 2, pp. 306–310, 2025, doi: &lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;10.1007/s11241-025-09441-z&lt;/a&gt;.</ieee>
<short>J. Shi, K.-H. Chen, Real-Time Systems 61 (2025) 306–310.</short>
<chicago>Shi, Junjie, and Kuan-Hsun Chen. “Shielded Reinforcement Learning for Fault-Tolerant Scheduling in Real-Time Systems.” &lt;i&gt;Real-Time Systems&lt;/i&gt; 61, no. 2 (2025): 306–10. &lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;https://doi.org/10.1007/s11241-025-09441-z&lt;/a&gt;.</chicago>
<mla>Shi, Junjie, and Kuan-Hsun Chen. “Shielded Reinforcement Learning for Fault-Tolerant Scheduling in Real-Time Systems.” &lt;i&gt;Real-Time Systems&lt;/i&gt;, vol. 61, no. 2, Springer Science and Business Media LLC, 2025, pp. 306–10, doi:&lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;10.1007/s11241-025-09441-z&lt;/a&gt;.</mla>
<ama>Shi J, Chen K-H. Shielded reinforcement learning for fault-tolerant scheduling in real-time systems. &lt;i&gt;Real-Time Systems&lt;/i&gt;. 2025;61(2):306-310. doi:&lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;10.1007/s11241-025-09441-z&lt;/a&gt;</ama>
<bibtex>@article{Shi_Chen_2025, title={Shielded reinforcement learning for fault-tolerant scheduling in real-time systems}, volume={61}, DOI={&lt;a href=&quot;https://doi.org/10.1007/s11241-025-09441-z&quot;&gt;10.1007/s11241-025-09441-z&lt;/a&gt;}, 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} }</bibtex>
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