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  5. Using gradient-based optimization for planning with deep Q-networks in parametrized action spaces

Using gradient-based optimization for planning with deep Q-networks in parametrized action spaces

Publication date
2025-11-21
Document type
Konferenzbeitrag
Author
Ehrhardt, Jonas
Schmidt, Johannes  
Heesch, René  
Niggemann, Oliver  
Organisational unit
Informatik im Maschinenbau  
DTEC.bw  
URL
https://ceur-ws.org/Vol-4103/paper5.pdf
URI
https://openhsu.ub.hsu-hh.de/handle/10.24405/22235
Conference
ECAI Workshop on AI-based Planning for Complex Real-World Applications 2025  
Project
Labor für die intelligente Leichtbauproduktion  
Engineering für die KI-basierte Automation in virtuellen und realen Produktionsumgebungen  
Publisher
RWTH Aachen
Book title
CAIPI 2025: ECAI 2025 Workshop on AI-based Planning for Complex Real-World Applications
First page
52
Last page
67
Is part of
https://openhsu.ub.hsu-hh.de/handle/10.24405/22239
Peer-reviewed
✅
Part of the university bibliography
✅
Additional Information
Language
English
Keyword
dtec.bw
Description
Published under the Creative Commons License Attribution 4.0 International (CC BY 4.0) (https://creativecommons.org/licenses/by/4.0/).
Version
Published version
Access right on openHSU
Metadata only access

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