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Action Space-Independent Exploration Methods in Multi-Agent Deep Reinforcement Learning for Wireless Power Allocation

; ;

In
2024 IEEE Wireless Communications and Networking Conference (WCNC) : proceedings : Dubai, AE, 21-24 April 2024 / general chair: Raed Shubair (NYU Abu Dhabi, United Arab Emirates) ; publications chair: Aryan Kaushik (University of Sussex, United Kingdom), Seiten/Artikel-Nr: 6 Seiten

Konferenz/Event:2024 IEEE Wireless Communications and Networking Conference , Dubai , U Arab Emirates , WCNC , 2024-04-21 - 2024-04-24

Impressum[Piscataway, NJ] : IEEE

Umfang6 Seiten

ISBN979-8-3503-0358-2, 979-8-3503-0359-9

Online
DOI: 10.1109/WCNC57260.2024.10571228


Einrichtungen

  1. Lehrstuhl für Verteilte Signalverarbeitung (612310)
  2. Profilbereich Mobility & Transport Engineering (MTE) (080021)



Dokumenttyp
Contribution to a book/Contribution to a conference proceedings

Format
online, print

Sprache
English

Anmerkung
Peer reviewed article

Externe Identnummern
SCOPUS: SCOPUS:2-s2.0-85198836562
WOS Core Collection: WOS:001268569304055

Interne Identnummern
RWTH-2024-09148
Datensatz-ID: 994171

Beteiligte Länder
Germany

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The record appears in these collections:
Document types > Events > Contributions to a conference proceedings
Document types > Books > Contributions to a book
Faculty of Electrical Engineering and Information Technology (Fac.6)
Central and Other Institutions
Public records
Publications database
080021
612310
 Record created 2024-09-30, last modified 2025-02-07
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