Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning

[2511.02605] Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning Skip to main content Search Submit Donate Log in Computer Science > Artificial Intelligence arXiv:2511.02605 (cs) [Submitted on 4 Nov 2025 ( v1 ), last revised 24 Aug 2026 (this version, v3)] Title: Adaptive GR(1) Specification Repair for Liveness-Preserving Shielding in Reinforcement Learning Authors: Tiberiu-Andrei Georgescu , Alexander W. Goodall , Dalal Alrajeh , Francesco Belardinelli , Sebastian Uchitel View PDF HTML (experimental) Abstract: Shielding is widely used to enforce safety in reinforcement learning (RL), ensuring that an agent's actions remain compliant with formal specifications. Classical shielding approaches, however, are often static, in the sense that they assume fixed logical specifications and hand-crafted abstractions. While these static shields provide safety under nominal assumptions, they fail to adapt when environment assumptions are violated. In this paper, we develop an adaptive shielding framework based on based on Generalized Reactivity of rank 1 (GR(1)) specifications, a tractable and expressive fragment of Linear Temporal Logic (LTL) that captures both safety and liveness properties. Our method detects environment assumption violations at runtime and employs Inductive Logic Programming (ILP) to automatically repair GR(1) specifications online, in a systematic and interpretable way. This ensures that the shield evolves gracefully, ensuring liveness is achievable and minimally weakening goals only when necessary. We consider two case studies: Minepump and Atari Seaquest; showing that (i) static symbolic controllers are often severely suboptimal when optimizing for auxiliary rewards, and (ii) RL agents equipped with our adaptive shield maintain near-optimal reward and perfect logical compliance compared with static shields. Comments: Accepted at NeSy 2026 Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2511.02605 [cs.AI] (or arXiv:2511.02605v3 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2511.02605 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Alexander W. Goodall [ view email ] [v1] Tue, 4 Nov 2025 14:27:28 UTC (1,169 KB) [v2] Fri, 20 Feb 2026 17:44:58 UTC (1,134 KB) [v3] Mon, 24 Aug 2026 20:59:41 UTC (1,117 KB) Full-text links: Access Paper: View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2025-11 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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