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Reinforcement Learning for Traffic Signal Control Optimization

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Metadaten
Author:Henri Meess, Jeremias GernerORCiD, Daniel Hein, Stefanie SchmidtnerORCiD, Gordon ElgerORCiD
Subtitle (English):A Concept for Real-World Implementation
Language:English
Document Type:Conference Paper
Conference:AAMAS ' 22: International Conference on Autonomous Agents and Multi-Agent Systems, Virtual Event New Zealand, 9.-13.05.2022
Year of first Publication:2022
published in (English):AAMAS '22: Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems
Publisher:International Foundation for Autonomous Agents and Multiagent Systems
Place of publication:Richland
ISBN:978-1-4503-9213-6
First Page:1699
Last Page:1701
Review:peer-review
Open Access:nein
Tag:DRL; MARL; Multi-Agent Reinforcement Learning in real-world; multimodal traffic; traffic optimization
URL:https://dl.acm.org/doi/10.5555/3535850.3536081
Faculties / Institutes / Organizations:Fakultät Elektro- und Informationstechnik
AImotion Bavaria
Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"
Release Date:2022/08/02