Optimal regret bounds for selecting the state representation in reinforcement learning.
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Standard
Optimal regret bounds for selecting the state representation in reinforcement learning. / Maillard, Odalric-Ambrym; Nguyen, Phuong; Ortner, Ronald et al.
JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning. 2013. S. 543-551.
JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning. 2013. S. 543-551.
Publikationen: Beitrag in Buch/Bericht/Konferenzband › Beitrag in Konferenzband
Harvard
Maillard, O-A, Nguyen, P, Ortner, R & Ryabko, D 2013, Optimal regret bounds for selecting the state representation in reinforcement learning. in JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning. S. 543-551.
APA
Maillard, O.-A., Nguyen, P., Ortner, R., & Ryabko, D. (2013). Optimal regret bounds for selecting the state representation in reinforcement learning. In JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning (S. 543-551)
Vancouver
Maillard OA, Nguyen P, Ortner R, Ryabko D. Optimal regret bounds for selecting the state representation in reinforcement learning. in JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning. 2013. S. 543-551
Author
Bibtex - Download
@inproceedings{d8b079e365a64cd6b7ab8739ec6b59e5,
title = "Optimal regret bounds for selecting the state representation in reinforcement learning.",
author = "Odalric-Ambrym Maillard and Phuong Nguyen and Ronald Ortner and Daniil Ryabko",
year = "2013",
language = "English",
pages = "543--551",
booktitle = "JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning",
}
RIS (suitable for import to EndNote) - Download
TY - GEN
T1 - Optimal regret bounds for selecting the state representation in reinforcement learning.
AU - Maillard, Odalric-Ambrym
AU - Nguyen, Phuong
AU - Ortner, Ronald
AU - Ryabko, Daniil
PY - 2013
Y1 - 2013
M3 - Conference contribution
SP - 543
EP - 551
BT - JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning
ER -