# Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control

arXiv: https://arxiv.org/abs/2303.06110 · HTML: https://mlsanity.com/paper/2303.06110/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2023.

## Built on by (most cited first, 12 of 12)

- [Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classification](https://mlsanity.com/paper/2502.02133/) (2025)
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024)
- [Reinforcement Learning for Sustainable Energy: A Survey](https://mlsanity.com/paper/2407.18597/) (2024)
- [GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems](https://mlsanity.com/paper/2410.05336/) (2024)
- [Grower-in-the-Loop Interactive Reinforcement Learning for Greenhouse Climate Control](https://mlsanity.com/paper/2505.23355/) (2025)
- [A TinyML Reinforcement Learning Approach for Energy-Efficient Light Control in Low-Cost Greenhouse Systems](https://mlsanity.com/paper/2512.01167/) (2025)
- [A Teacher-Student MPC-PPO Coupled Reinforcement Learning Framework for Winter Temperature Control of Solar Greenhouses in Northern China](https://mlsanity.com/paper/2504.20815/) (2025)
- [To Learn or Not to Learn: A Litmus Test for Using Reinforcement Learning in Control](https://mlsanity.com/paper/2604.11463/) (2026)
- [Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control](https://mlsanity.com/paper/2607.07365/) (2026)
- [Calibration-First Reward-Component Auditing for Reinforcement Learning Control in Smart Greenhouses](https://mlsanity.com/paper/2607.11959/) (2026)
- [LettuceVisSim: A Simulator That Generates Lettuce Image Time-series for Vision-Based Reinforcement Learning](https://mlsanity.com/paper/2609.12505/) (2026)
- [Safe Greenhouse Climate Control Using Lagrangian-Constrained PPO with Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2609.34966/) (2026)

## Abstract (arXiv)

Greenhouse is an important protected horticulture system for feeding the world with enough fresh food. However, to maintain an ideal growing climate in a greenhouse requires resources and operational costs. In order to achieve economical and sustainable crop growth, efficient climate control of greenhouse production becomes essential. Model Predictive Control (MPC) is the most commonly used approach in the scientific literature for greenhouse climate control. However, with the developments of sensing and computing techniques, reinforcement learning (RL) is getting increasing attention recently. With each control method having its own way to state the control problem, define control goals, and seek for optimal control actions, MPC and RL are representatives of model-based and learning-based control approaches, respectively. Although researchers have applied certain forms of MPC and RL to control the greenhouse climate, very few effort has been allocated to analyze connections, differences, pros and cons between MPC and RL either from a mathematical or performance perspective. Therefore, this paper will 1) propose MPC and RL approaches for greenhouse climate control in an unified framework; 2) analyze connections and differences between MPC and RL from a mathematical perspective; 3) compare performance of MPC and RL in a simulation study and afterwards present and interpret comparative results into insights for the application of the different control approaches in different scenarios.