Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
In: Optimal Offloading Strategies for Edge-Computing via Mean-Field Games and Control, GLOBECOM 2022 - 2022 IEEE Global Communications Conference, IEEE, ISBN 978-1-6654-3540-6
- Ereignis
-
Veröffentlichung
- (wo)
-
Darmstadt
- (wer)
-
Universitäts- und Landesbibliothek
- (wann)
-
2024
- Urheber
- DOI
-
10.26083/tuprints-00028855
- URN
-
urn:nbn:de:tuda-tuprints-288553
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:21 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Cui, Kai
- Yilmaz, Mustafa Burak
- Tahir, Anam
- Klein, Anja
- Koeppl, Heinz W.
- Universitäts- und Landesbibliothek
Entstanden
- 2024
Ähnliche Objekte (12)
Solitary states in the mean-field limit
Motif-based mean-field approximation of interacting particles on clustered networks
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Hypergraphon mean field games
Learning Sparse Graphon Mean Field Games
Large-Scale Multi-Agent Reinforcement Learning via Mean Field Games
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Solitary states in the mean-field limit
Motif-based mean-field approximation of interacting particles on clustered networks
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Hypergraphon mean field games
Learning Sparse Graphon Mean Field Games
Large-Scale Multi-Agent Reinforcement Learning via Mean Field Games
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons
Learning Mean Field Games on Sparse Graphs: A Hybrid Graphex Approach
Solitary states in the mean-field limit
Motif-based mean-field approximation of interacting particles on clustered networks
Learning Mean-Field Control for Delayed Information Load Balancing in Large Queuing Systems
Scalable Task-Driven Robotic Swarm Control via Collision Avoidance and Learning Mean-Field Control
Learning Graphon Mean Field Games and Approximate Nash Equilibria
Approximately Solving Mean Field Games via Entropy-Regularized Deep Reinforcement Learning
The Hawkes Edge Partition Model for Continuous-time Event-based Temporal Networks
Hypergraphon mean field games
Learning Sparse Graphon Mean Field Games
Large-Scale Multi-Agent Reinforcement Learning via Mean Field Games
Mean Field Games on Weighted and Directed Graphs via Colored Digraphons