Multi-step first: A lightweight deep reinforcement learning strategy for robust continuous control with partial observability.
- DOI
- 10.1016/j.neunet.2025.108521
- Published
- 2026 Jul
- Container
- Neural networks : the official journal of the International Neural Network Society
- Publisher
- Not recorded
- Open access
- unknown
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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.2025.108521,
title = {Multi-step first: A lightweight deep reinforcement learning strategy for robust continuous control with partial observability.},
author = {Meng L and Gorbet R and Burke M and Kulić D},
year = {2026},
journal = {Neural networks : the official journal of the International Neural Network Society},
doi = {10.1016/j.neunet.2025.108521},
url = {https://doi.org/10.1016/j.neunet.2025.108521}
}RIS
TY - JOUR TI - Multi-step first: A lightweight deep reinforcement learning strategy for robust continuous control with partial observability. AU - Meng L AU - Gorbet R AU - Burke M AU - Kulić D PY - 2026 JO - Neural networks : the official journal of the International Neural Network Society DO - 10.1016/j.neunet.2025.108521 UR - https://doi.org/10.1016/j.neunet.2025.108521 ER -
APA
L, M., R, G., M, B., & D, K. (2026). Multi-step first: A lightweight deep reinforcement learning strategy for robust continuous control with partial observability.. Neural networks : the official journal of the International Neural Network Society. https://doi.org/10.1016/j.neunet.2025.108521
Source records
- pubmed · retrieved 2026-09-26T07:44:38.761Z