Record link: 
http://hdl.handle.net/20.500.12708/219594
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Title: 
Rule-Guided Reinforcement Learning Policy Evaluation and Improvement
en
Citation: 
Tappler, M., Lopez-Miguel, I. D., Tschiatschek, S., & Bartocci, E. (2025). Rule-Guided Reinforcement Learning Policy Evaluation and Improvement. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence (pp. 6254–6262). https://doi.org/10.24963/ijcai.2025/696
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Publisher DOI: 
10.24963/ijcai.2025/696
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Tappler, Martin 
Lopez-Miguel, Ignacio D. 
Tschiatschek, Sebastian 
Bartocci, Ezio 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
E056-13 - Fachbereich LogiCS 
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems 
E056-26 - Fachbereich Automated Reasoning 
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Published in: 
Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence 
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ISBN: 
978-1-956792-06-5
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Date (published): 
2025
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Event name: 
34th International Joint Conference on Artificial Intelligence (IJCAI) 
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Event date: 
16-Aug-2025 - 22-Aug-2025
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Event place: 
Montreal, Canada
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Number of Pages: 
9
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Peer reviewed: 
Yes
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Keywords: 
rule-based learning; reinforcement learning; Explainable AI
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Project title: 
Training and Guiding AI Agents with Ethical Rules: ICT22-023 (WWTF Wiener Wissenschafts-, Forschu und Technologiefonds) 
Logics for Computer Science Program at TU Wien: 101034440 (European Commission) 
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
Conference Paper

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