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Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes
Research article (IEEE Open Journal of Control Systems, 2023) · cited 33× · AI/ML
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Summary
Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes is a scholarly article[1].
Key Facts
Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes's instance of is recorded as scholarly article[2].
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APA4ort.xyz Knowledge Graph. (2026). Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes. Retrieved May 24, 2026, from https://4ort.xyz/entity/provably-safe-reinforcement-learning-via-action-projection-using-reachability-analysis-and-polynomial-zonotopes
MLA“Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/provably-safe-reinforcement-learning-via-action-projection-using-reachability-analysis-and-polynomial-zonotopes.
BibTeX@misc{4ortxyz_provably-safe-reinforcement-learning-via-action-projection-using-reachability-analysis-and-polynomial-zonotopes_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes}}, year = {2026}, url = {https://4ort.xyz/entity/provably-safe-reinforcement-learning-via-action-projection-using-reachability-analysis-and-polynomial-zonotopes}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Provably Safe Reinforcement Learning via Action Projection Using Reachability Analysis and Polynomial Zonotopes — https://4ort.xyz/entity/provably-safe-reinforcement-learning-via-action-projection-using-reachability-analysis-and-polynomial-zonotopes (retrieved 2026-05-24)
