---
res:
  bibo_abstract:
  - The safety of Reinforcement Learning (RL)-based controllers has become a prominent
    research area in recent years, with various approaches being proposed to address
    this critical issue. Runtime Safety Assurance (RSA) methods for RL, such as Shielded
    RL, provide formal safety guarantees by preventing agents from taking unsafe actions
    and suggesting safe alternatives when necessary. However, previous surveys and
    reviews on RSA for RL have not thoroughly analysed the challenges and applications
    within the industrial sector. This study builds on existing state-of-the-art research
    on Shielded RL methods, emphasising its contributions to industrial applications
    and offering a domain-specific categorisation. This categorisation highlights
    the primary industrial domains utilising Shielded RL, detailing the optimised
    functions achieved by RL and the safety functions ensured by the shield. Additionally,
    the study presents a categorisation based on environmental features, enabling
    readers to assess the complexity of the problems addressed by the techniques studied.
    The shield’s attributes are analysed for each work, identifying key trends in
    their application, including their adaptability to new scenarios. Finally, a basic
    categorisation model for Shielded RL approaches, grounded in industrial safety
    standards, is introduced. This model serves as a baseline for future studies aiming
    to evaluate the maturity level of the works reviewed.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Haritz
      foaf_name: Odriozola-Olalde, Haritz
      foaf_surname: Odriozola-Olalde
  - foaf_Person:
      foaf_givenName: Filip
      foaf_name: Cano Cordoba, Filip
      foaf_surname: Cano Cordoba
      foaf_workInfoHomepage: http://www.librecat.org/personId=708cad98-e86a-11ef-8098-bdae2d7c6af1
    orcid: 0000-0002-0783-904X
  - foaf_Person:
      foaf_givenName: Bettina
      foaf_name: Könighofer, Bettina
      foaf_surname: Könighofer
  - foaf_Person:
      foaf_givenName: Nestor
      foaf_name: Arana-Arexolaleiba, Nestor
      foaf_surname: Arana-Arexolaleiba
  - foaf_Person:
      foaf_givenName: Maider
      foaf_name: Zamalloa, Maider
      foaf_surname: Zamalloa
  - foaf_Person:
      foaf_givenName: Jon
      foaf_name: Perez-Cerrolaza, Jon
      foaf_surname: Perez-Cerrolaza
  bibo_doi: 10.1016/j.iswa.2026.200729
  bibo_volume: 32
  dct_date: 2026^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/2667-3053
  dct_language: eng
  dct_publisher: Elsevier@
  dct_subject:
  - Reinforcement learning
  - Runtime safety assurance
  - Shield
  - Safety
  - Industrial application
  dct_title: 'Shielded reinforcement learning for industrial applications: A systematic
    literature survey@'
...