---
res:
  bibo_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>@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Junjie
      foaf_name: Shi, Junjie
      foaf_surname: Shi
  - foaf_Person:
      foaf_givenName: Kuan-Hsun
      foaf_name: Chen, Kuan-Hsun
      foaf_surname: Chen
  bibo_doi: 10.1007/s11241-025-09441-z
  bibo_issue: '2'
  bibo_volume: 61
  dct_date: 2025^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0922-6443
  - http://id.crossref.org/issn/1573-1383
  dct_language: eng
  dct_publisher: Springer Science and Business Media LLC@
  dct_title: Shielded reinforcement learning for fault-tolerant scheduling in real-time
    systems@
...