mlsanityData to 14 Oct 2026

A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China

arXiv 2504.20815 ↗First posted 29 Apr 2025math.OCcited by 0

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

Abstract (arXiv)

Solar greenhouses are crucial infrastructure of modern agricultural production in northern China. However, highly fluctuating temperature in winter season results in poor greenhouse temperature control, which affects crop growth and increases energy consumption. To tackle these challenges, an advanced control system that can efficiently optimize multiple objectives under dramatic climate conditions is essential. Therefore, this study propose a model predictive control-coupled proximal policy optimization (MPC-PPO) control framework. A teacher-student control framework is constructed in which the MPC generating high-quality control experiences to guide the PPO agent's learning process. An adaptive dynamic weighting mechanism is employed to balance the influence of MPC experiences during PPO training. Evaluation conducted in solar greenhouses across three provinces in northern China (Beijing, Hebei, and Shandong) demonstrates that: (1) the MPC-PPO method achieved the highest temperature control performance (96.31 on a 100-point scale), with a 5.46-point improvement compared to the non-experience integration baseline, when reduced standard deviation by nearly half and enhanced exploration efficiency; (2) the MPC-PPO method achieved a ventilation control reward of 99.19, optimizing ventilation window operations with intelligent time-differentiated strategies that reduced energy loss during non-optimal hours; (3) feature analysis reveals that historical window opening, air temperature, and historical temperature are the most influential features for effective control, i.e., SHAP values of 7.449, 4.905, and 4.747 respectively; and (4) cross-regional tests indicated that MPC-PPO performs best in all test regions, confirming generalization of the method.

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 5 of its 29 references that are in the map. First posted on arXiv 29 Apr 2025.

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5 of 29 references.

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Same direction, newest first

  1. 2024Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control19 Sep

Other references · 4

  1. 2020Multi-Agent Trust Region Policy Optimizationcited by 20
  2. 2021Convergence of Deep ReLU Networkscited by 15
  3. 2023Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Controlcited by 12
  4. 2021Generalized Reinforcement Learning for Building Control using Behavioral Cloningcited by 1

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  1. 2026Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks
  2. 2025RL-Guided MPC for Autonomous Greenhouse Control
  3. 2026Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control
  4. 2025Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control
  5. 2024GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems
  6. 2026Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses
  7. 2026Reinforcement learning-based dynamic cleaning scheduling framework for solar energy system
  8. 2024Learning-based agricultural management in partially observable environments subject to climate variability