Gunasekaran, Yeshwanth D.; Rahman, Md Farhadur; Hasani, Sona; Zhang, Nan; Das, Gautam
(, Proceedings of the 2018 International Conference on Management of Data)
Sun, Xiaowu, Fatnassi, Wael, Cruz, Ulices Santa, and Shoukry, Yasser. Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach. Retrieved from https://par.nsf.gov/biblio/10379945. 2021 60th IEEE Conference on Decision and Control (CDC) . Web. doi:10.1109/CDC45484.2021.9683009.
Sun, Xiaowu, Fatnassi, Wael, Cruz, Ulices Santa, and Shoukry, Yasser.
"Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach". 2021 60th IEEE Conference on Decision and Control (CDC) (). Country unknown/Code not available. https://doi.org/10.1109/CDC45484.2021.9683009.https://par.nsf.gov/biblio/10379945.
@article{osti_10379945,
place = {Country unknown/Code not available},
title = {Provably Safe Model-Based Meta Reinforcement Learning: An Abstraction-Based Approach},
url = {https://par.nsf.gov/biblio/10379945},
DOI = {10.1109/CDC45484.2021.9683009},
abstractNote = {},
journal = {2021 60th IEEE Conference on Decision and Control (CDC)},
author = {Sun, Xiaowu and Fatnassi, Wael and Cruz, Ulices Santa and Shoukry, Yasser},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.