Verifiably safe exploration for end-to-end reinforcement learning
Name
3447928.3456653.pdf
Description
Published version
Size
969.44 KB
Format
Adobe PDF
Checksum (MD5)
8d84030ad8beec5326a7c040d9d8d6eb
Author(s)
Hunt, Nathan
Fulton, Nathan
Magliacane, Sara
Hoang, Trong Nghia
Das, Subhro
Solar-Lezama, Armando
Date Issued
2021
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
MIT Department
MIT-IBM Watson AI Lab
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3447928.3456653
