The Most Overestimated <i>Q</i> Value Regularization in High-Dimensional Discrete Action Spaces for Offline Reinforcement Learning
- DOI
- 10.1109/tnnls.2025.3640101
- Published
- 2026-06
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2025.3640101,
title = {The Most Overestimated
<i>Q</i>
Value Regularization in High-Dimensional Discrete Action Spaces for Offline Reinforcement Learning},
author = {Seunghwan Yu and Homin Park and Byungjin Ko and Jisub Shin and Yoonki Hong and Taejoon Park and Jong-Wan Yoon},
year = {2026},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2025.3640101},
url = {https://doi.org/10.1109/tnnls.2025.3640101}
}RIS
TY - JOUR
TI - The Most Overestimated
<i>Q</i>
Value Regularization in High-Dimensional Discrete Action Spaces for Offline Reinforcement Learning
AU - Seunghwan Yu
AU - Homin Park
AU - Byungjin Ko
AU - Jisub Shin
AU - Yoonki Hong
AU - Taejoon Park
AU - Jong-Wan Yoon
PY - 2026
JO - IEEE Transactions on Neural Networks and Learning Systems
DO - 10.1109/tnnls.2025.3640101
UR - https://doi.org/10.1109/tnnls.2025.3640101
ER - APA
Yu, S., Park, H., Ko, B., Shin, J., Hong, Y., Park, T., & Yoon, J. (2026). The Most Overestimated <i>Q</i> Value Regularization in High-Dimensional Discrete Action Spaces for Offline Reinforcement Learning. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2025.3640101
Source records
- crossref · retrieved 2026-09-25T17:08:58.068Z