I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
Monotonic Quantile Network for Worst-Case Offline Reinforce..:
Bai, Chenjia
;
Xiao, Ting
;
Zhu, Zhoufan
...
IEEE Transactions on Neural Networks and Learning Systems. 35 (2024) 7 - p. 8954-8968 , 2024
Link:
https://doi.org/10.1109/tnnls.2022.3217189
RT Journal T1 <Title>Monotonic Quantile Network for Worst-Case Offline Reinforcement Learning</Title> UL https://suche.suub.uni-bremen.de/peid=cr-10.1109_tnnls.2022.3217189&Exemplar=1&LAN=DE A1 Bai, Chenjia A1 Xiao, Ting A1 Zhu, Zhoufan A1 Wang, Lingxiao A1 Zhou, Fan A1 Garg, Animesh A1 He, Bin A1 Liu, Peng A1 Wang, Zhaoran PB Institute of Electrical and Electronics Engineers (IEEE) YR 2024 SN 2162-237X SN 2162-2388 JF IEEE Transactions on Neural Networks and Learning Systems VO 35 IS 7 SP 8954 OP 8968 LK http://dx.doi.org/https://doi.org/10.1109/tnnls.2022.3217189 DO https://doi.org/10.1109/tnnls.2022.3217189 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
