Title: Towards Scalable Verification of Deep Reinforcement Learning
Authors: Amir, Guy 
Schapira, Michael 
Katz, Guy 
Issue Date: Oct-2021
Citation: 
Amir, G., Schapira, M., & Katz, G. (2021). Towards Scalable Verification of Deep Reinforcement Learning. In Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021 (pp. 193–203). TU Wien Academic Press. https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_28
Book Title: Proceedings of the 21st Conference on Formal Methods in Computer-Aided Design – FMCAD 2021 
Series: Conference Series: Formal Methods in Computer-Aided Design 
Keywords: formal methods; computer-aided system design; hardware and system verification
formale Methode; rechnerunterstützte Systementwicklung; Hardwareverifikation
URI: http://hdl.handle.net/20.500.12708/18630
https://doi.org/10.34727/2021/isbn.978-3-85448-046-4_28
DOI: 10.34727/2021/isbn.978-3-85448-046-4_28
Book DOI: 10.34727/2021/isbn.978-3-85448-046-4
License: CC BY 4.0 CC BY 4.0
Publication Type: Inproceedings
Appears in Collections:Open Access Series
Conference Paper

Files in this item:


Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons