A digital twin-driven multi-agent deep reinforcement learning framework for synergistic resource scheduling in revolutionary heritage and sports tourism integration.
- DOI
- 10.1038/s41598-026-60132-6
- Published
- 2026 Jul 7
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-60132-6,
title = {A digital twin-driven multi-agent deep reinforcement learning framework for synergistic resource scheduling in revolutionary heritage and sports tourism integration.},
author = {Zhou S and Ji Z and Nie X},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-60132-6},
url = {https://doi.org/10.1038/s41598-026-60132-6}
}RIS
TY - JOUR TI - A digital twin-driven multi-agent deep reinforcement learning framework for synergistic resource scheduling in revolutionary heritage and sports tourism integration. AU - Zhou S AU - Ji Z AU - Nie X PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-60132-6 UR - https://doi.org/10.1038/s41598-026-60132-6 ER -
APA
S, Z., Z, J., & X, N. (2026). A digital twin-driven multi-agent deep reinforcement learning framework for synergistic resource scheduling in revolutionary heritage and sports tourism integration.. Scientific reports. https://doi.org/10.1038/s41598-026-60132-6
Source records
- pubmed · retrieved 2026-09-25T15:24:39.358Z