mlsanityData to 14 Oct 2026

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

arXiv 2607.11959 ↗First posted 12 Jul 2026cs.AIcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 11 of its 33 references that are in the map. First posted on arXiv 12 Jul 2026.

Builds on

11 of 33 references.

Closest ancestors · 3

Same direction, newest first

  1. 2025Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control29 May
  2. 2024GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems6 Oct
  3. 2024Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control19 Sep

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
  3. 2018Verifiable Reinforcement Learning via Policy Extractioncited by 177
  4. 2020Explainable Reinforcement Learning: A Surveycited by 141
  5. 2021Robot Learning from Randomized Simulations: A Reviewcited by 63
  6. 2023Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Controlcited by 12
  7. 2019Distributional Reward Decomposition for Reinforcement Learningcited by 11
  8. 2021Robust Model-based Reinforcement Learning for Autonomous Greenhouse Controlcited by 6

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks
  2. 2025A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China
  3. 2025A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems
  4. 2025RL-Guided MPC for Autonomous Greenhouse Control
  5. 2026Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control
  6. 2024Learning-based agricultural management in partially observable environments subject to climate variability
  7. 2024Using reinforcement learning to improve drone-based inference of greenhouse gas fluxes
  8. 2024Intelligent Agricultural Management Considering N2O Emission and Climate Variability with Uncertainties