APO: Anchored policy optimization by leveraging unsampled actions in continuous spaces.
- DOI
- 10.1016/j.neunet.2026.109476
- Published
- 2026 Aug 5
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.neunet.2026.109476,
title = {APO: Anchored policy optimization by leveraging unsampled actions in continuous spaces.},
author = {Luo W and Liu Y and Tang H and Chen B and Yang C and Xiang L and He Z},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2026.109476},
url = {https://doi.org/10.1016/j.neunet.2026.109476}
}RIS
TY - JOUR TI - APO: Anchored policy optimization by leveraging unsampled actions in continuous spaces. AU - Luo W AU - Liu Y AU - Tang H AU - Chen B AU - Yang C AU - Xiang L AU - He Z PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2026.109476 UR - https://doi.org/10.1016/j.neunet.2026.109476 ER -
APA
W, L., Y, L., H, T., B, C., C, Y., L, X., & Z, H. (2026). APO: Anchored policy optimization by leveraging unsampled actions in continuous spaces.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2026.109476
Source records
- pubmed · retrieved 2026-09-26T00:17:15.625Z