Deep Reinforcement Learning for Adaptive Temperature Control in Greenhouses: A DQN-Based Framework and Experimental Evaluation
- DOI
- 10.21203/rs.3.rs-11563502/v1
- Published
- 2026-11-02
- Container
- Not recorded
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.21203/rs.3.rs-11563502/v1,
title = {Deep Reinforcement Learning for Adaptive Temperature Control in Greenhouses: A DQN-Based Framework and Experimental Evaluation},
author = {Priyanshi Kumari and Hamed laouz and Shahana Bano},
year = {2026},
doi = {10.21203/rs.3.rs-11563502/v1},
url = {https://doi.org/10.21203/rs.3.rs-11563502/v1}
}RIS
TY - JOUR TI - Deep Reinforcement Learning for Adaptive Temperature Control in Greenhouses: A DQN-Based Framework and Experimental Evaluation AU - Priyanshi Kumari AU - Hamed laouz AU - Shahana Bano PY - 2026 DO - 10.21203/rs.3.rs-11563502/v1 UR - https://doi.org/10.21203/rs.3.rs-11563502/v1 ER -
APA
Kumari, P., laouz, H., & Bano, S. (2026). Deep Reinforcement Learning for Adaptive Temperature Control in Greenhouses: A DQN-Based Framework and Experimental Evaluation. https://doi.org/10.21203/rs.3.rs-11563502/v1
Source records
- crossref · retrieved 2026-11-07T14:01:48.650Z