A scalable approach to optimize traffic signal control with federated reinforcement learning
- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
-
1 Online-Ressource.
- Language
-
Englisch
- Bibliographic citation
-
A scalable approach to optimize traffic signal control with federated reinforcement learning ; volume:13 ; number:1 ; day:6 ; month:11 ; year:2023 ; pages:1-11 ; date:12.2023
Scientific reports ; 13, Heft 1 (6.11.2023), 1-11, 12.2023
- Creator
-
Bao, Jingjing
Wu, Celimuge
Lin, Yangfei
Zhong, Lei
Chen, Xianfu
Yin, Rui
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1038/s41598-023-46074-3
- URN
-
urn:nbn:de:101:1-2024012309233368322357
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:22 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Bao, Jingjing
- Wu, Celimuge
- Lin, Yangfei
- Zhong, Lei
- Chen, Xianfu
- Yin, Rui
- SpringerLink (Online service)
Other Objects (12)
Federated reinforcement learning for the edge
Distributed Deep Reinforcement Learning for Learn-to-optimize
Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing
Incremental federated learning for traffic flow classification in heterogeneous data scenarios
Potential of traffic information to optimize route and departure time choice
Potential of traffic information to optimize route and departure time choice
Deep reinforcement learning framework to optimize energy supply for electric earthwork processes
Core network traffic prediction based on vertical federated learning and split learning
An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective
Efficient and scalable reinforcement learning for large-scale network control
Predicting traffic noise using land-use regression—a scalable approach
An event-driven multi agent system for scalable traffic optimization
Federated reinforcement learning for the edge
Distributed Deep Reinforcement Learning for Learn-to-optimize
Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing
Incremental federated learning for traffic flow classification in heterogeneous data scenarios
Potential of traffic information to optimize route and departure time choice
Potential of traffic information to optimize route and departure time choice
Deep reinforcement learning framework to optimize energy supply for electric earthwork processes
Core network traffic prediction based on vertical federated learning and split learning
An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective
Efficient and scalable reinforcement learning for large-scale network control
Predicting traffic noise using land-use regression—a scalable approach
An event-driven multi agent system for scalable traffic optimization
Federated reinforcement learning for the edge
Distributed Deep Reinforcement Learning for Learn-to-optimize
Reinforcement learning based task scheduling for environmentally sustainable federated cloud computing
Incremental federated learning for traffic flow classification in heterogeneous data scenarios
Potential of traffic information to optimize route and departure time choice
Potential of traffic information to optimize route and departure time choice
Deep reinforcement learning framework to optimize energy supply for electric earthwork processes
Core network traffic prediction based on vertical federated learning and split learning
An efficient personalized federated learning approach in heterogeneous environments: a reinforcement learning perspective
Efficient and scalable reinforcement learning for large-scale network control
Predicting traffic noise using land-use regression—a scalable approach