Verifiably safe exploration for end-to-end reinforcement learning

Author(s)
Hunt, Nathan; Fulton, Nathan; Magliacane, Sara; Hoang, Trong Nghia; Das, Subhro; ... Show more
Thumbnail
DownloadPublished version (969.4Kb)
Publisher with Creative Commons License
Terms of use
Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/
Metadata
Show full item record
Date issued
2021
URI
https://hdl.handle.net/1721.1/143887
Department
MIT-IBM Watson AI Lab; Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Journal
Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control
Publisher
Association for Computing Machinery (ACM)
Citation
Hunt, Nathan, Fulton, Nathan, Magliacane, Sara, Hoang, Trong Nghia, Das, Subhro et al. 2021. "Verifiably safe exploration for end-to-end reinforcement learning." Proceedings of the 24th International Conference on Hybrid Systems: Computation and Control.
Version: Final published version
Collections