[Submitted on 12 Jul 2026]
Title:Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses
View a PDF of the paper titled Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses, by Yuhui Bie and 2 other authors
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Abstract:Greenhouse reinforcement learning can test climate-control ideas at a speed and scale that is difficult to achieve with crop experiments alone. For smart-greenhouse control, however, a single simulator return is not enough: a grower or control engineer also needs to know when the policy heats, enriches CO2, vents, manages humidity, deploys screens, or uses this http URL propose a reproducible calibration-first reward audit framework that keeps named greenhouse-control reward components comparable across simulator training, facility-adapted rollouts, logged Autonomous Greenhouse Challenge records, and actuator-rule distillation. In GreenLight-Gym, the framework decomposes the scalar reward into conditional temperature, CO2, humidity and vapor-pressure-deficit, screen, and actuation-proxy terms; adapts GreenLight to the second Autonomous Greenhouse Challenge logged climate traces; and scores the same components on logged greenhouse data.
Comments: 28 pages, 8 figures
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.11959 [cs.AI]
(or arXiv:2607.11959v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2607.11959
arXiv-issued DOI via DataCite
Submission history
From: Yaojun Wang [view email] [v1] Sun, 12 Jul 2026 07:37:54 UTC (528 KB)
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