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
AdverSAR: Adversarial Search and Rescue via Multi-Agent Rei..:
, In:
2022 IEEE International Symposium on Technologies for Homeland Security (HST)
,
Rahman, Aowabin
;
Bhattacharya, Arnab
;
Ramachandran, Thiagarajan
... - p. 1-7 , 2022
Link:
https://doi.org/10.1109/HST56032.2022.10025434
RT T1 <cTitle>2022 IEEE International Symposium on Technologies for Homeland Security (HST)</cTitle>: T1 <Title>AdverSAR: Adversarial Search and Rescue via Multi-Agent Reinforcement Learning</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-10025434&Exemplar=1&LAN=DE A1 Rahman, Aowabin A1 Bhattacharya, Arnab A1 Ramachandran, Thiagarajan A1 Mukherjee, Sayak A1 Sharma, Himanshu A1 Fujimoto, Ted A1 Chatterjee, Samrat YR 2022 K1 Training K1 Robot kinematics K1 Collaboration K1 Prototypes K1 Reinforcement learning K1 US Department of Homeland Security K1 Robot sensing systems K1 Search and Rescue K1 Multi-agent Reinforcement Learning K1 Adversarial Reinforcement Learning K1 Critical Infrastructure Security SP 1 OP 7 LK http://dx.doi.org/https://doi.org/10.1109/HST56032.2022.10025434 DO https://doi.org/10.1109/HST56032.2022.10025434 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
