Record link: 
http://hdl.handle.net/20.500.12708/218186
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Title: 
Scalable Offline Reinforcement Learning for Mean Field Games
en
Citation: 
Brunnbauer, A., Lemmel, J., Babaiee, Z., Neubauer, S., & Grosu, R. (2025). Scalable Offline Reinforcement Learning for Mean Field Games. In S. Das, A. Nowé, & Y. Vorobeychik (Eds.), Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, {AAMAS} 2025, Detroit, MI, USA, May 19-23, 2025 (pp. 408–417). International Foundation for Autonomous Agents and Multiagent Systems. https://doi.org/10.5555/3709347.3743555
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Publisher DOI: 
10.5555/3709347.3743555
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Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
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Authors: 
Brunnbauer, Axel 
Lemmel, Julian 
Babaiee, Zahra 
Neubauer, Sophie 
Grosu, Radu 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems 
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Published in: 
Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, {AAMAS} 2025, Detroit, MI, USA, May 19-23, 2025 
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ISBN: 
979-8-4007-1426-9
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Date (published): 
5-Jun-2025
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Event name: 
24th International Conference on Autonomous Agents and Multiagent System (AAMAS 2025) 
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Event date: 
19-May-2025 - 23-May-2025
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Event place: 
Detroit, MI, United States of America (the)
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Number of Pages: 
10
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Publisher: 
International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC
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Peer reviewed: 
Yes
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Keywords: 
Reinforcement Learning; Mean Field Games; Offline Munchausen Mirror Descent
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Additional information: 
https://dl.acm.org/doi/10.5555/3709347.3743555
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
2020 - Elektrotechnik, Elektronik, Informationstechnik: 100%
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Appears in Collections:
Conference Paper

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