<div class="csl-bib-body">
<div class="csl-entry">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 <i>The Fourteenth International Conference on Learning Representations : ICLR 2026</i>. The Fourteenth International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil.</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/229372
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dc.description
https://iclr.cc/Conferences/2026
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dc.language.iso
en
-
dc.subject
Relative Entropy Pathwise Policy Optimization
en
dc.subject
On‑policy reinforcement learning
en
dc.subject
Q‑based policy gradients
en
dc.subject
Score‑function methods
en
dc.subject
Variance reduction in policy gradient estimation
en
dc.title
Relative Entropy Pathwise Policy Optimization
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.contributor.affiliation
University of Warsaw, Poland
-
dc.contributor.affiliation
University of California, Berkeley, United States of America (the)
-
dc.type.category
Full-Paper Contribution
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tuw.booktitle
The Fourteenth International Conference on Learning Representations : ICLR 2026
-
tuw.peerreviewed
true
-
tuw.researchTopic.id
I2
-
tuw.researchTopic.name
Computer Engineering and Software-Intensive Systems
-
tuw.researchTopic.value
100
-
tuw.publication.orgunit
E191-01 - Forschungsbereich Cyber-Physical Systems
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tuw.publication.orgunit
E056-17 - Fachbereich Trustworthy Autonomous Cyber-Physical Systems
