Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses

Yuhui Bie; Guowei Xu; Yaojun Wang

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arXiv · 2607.11959

Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses

Yuhui Bie·Guowei Xu·Yaojun Wang

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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 lamps.We 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.

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Yuhui Bie, Guowei Xu, Yaojun Wang. 2026-07-12. Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses. https://arxiv.org/abs/2607.11959

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