Record link: 
http://hdl.handle.net/20.500.12708/229372
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Title: 
Relative Entropy Pathwise Policy Optimization
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Citation: 
Voelcker, C., Brunnbauer, A., Hussing, M., Nauman, M., Abbeel, P., Eaton, E., Grosu, R., Farahmand, A., & Gilitschenski, I. (2026). Relative Entropy Pathwise Policy Optimization. In The Fourteenth International Conference on Learning Representations : ICLR 2026. The Fourteenth International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil.
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Publication Type: 
Inproceedings - Full-Paper Contribution
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Language: 
English
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Authors: 
Voelcker, Claas 
Brunnbauer, Axel 
Hussing, Marcel 
Nauman, Michal 
Abbeel, Pieter 
Eaton, Eric 
Grosu, Radu 
Farahmand, Amir-massoud 
Gilitschenski, Igor 
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Organisational Unit: 
E191-01 - Forschungsbereich Cyber-Physical Systems 
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems 
E056-28 - Fachbereich Computational Sustainability 
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Published in: 
The Fourteenth International Conference on Learning Representations : ICLR 2026 
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Date (published): 
26-Jan-2026
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Event name: 
The Fourteenth International Conference on Learning Representations (ICLR 2026) 
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Event date: 
23-Apr-2026 - 25-Apr-2026
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Event place: 
Rio de Janeiro, Brazil
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Number of Pages: 
30
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Peer reviewed: 
Yes
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Keywords: 
Relative Entropy Pathwise Policy Optimization; On‑policy reinforcement learning; Q‑based policy gradients; Score‑function methods; Variance reduction in policy gradient estimation
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Additional information: 
https://iclr.cc/Conferences/2026
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Research Areas: 
Computer Engineering and Software-Intensive Systems: 100%
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Science Branch: 
1020 - Informatik: 100%
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Appears in Collections:
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