An improved Q-learning approach for rescue path planning in mass casualty incidents under damaged road network conditions.

Wang S, Yang J, Yang P.

Open source

DOI: 10.1038/s41598-026-50845-z
Published: 2026-05-27
Container: Sci Rep
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1038/s41598-026-50845-z,
  title = {An improved Q-learning approach for rescue path planning in mass casualty incidents under damaged road network conditions.},
  author = {Wang S and  Yang J and  Yang P.},
  year = {2026},
  journal = {Sci Rep},
  doi = {10.1038/s41598-026-50845-z},
  url = {https://doi.org/10.1038/s41598-026-50845-z}
}

RIS

TY  - JOUR
TI  - An improved Q-learning approach for rescue path planning in mass casualty incidents under damaged road network conditions.
AU  - Wang S
AU  -  Yang J
AU  -  Yang P.
PY  - 2026
JO  - Sci Rep
DO  - 10.1038/s41598-026-50845-z
UR  - https://doi.org/10.1038/s41598-026-50845-z
ER  -

APA

S, W., J, Y., & P., Y. (2026). An improved Q-learning approach for rescue path planning in mass casualty incidents under damaged road network conditions.. Sci Rep. https://doi.org/10.1038/s41598-026-50845-z

Source records

europe-pmc · retrieved 2026-09-26T03:58:33.219Z
doaj · retrieved 2026-09-26T03:58:33.192Z