# Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control

arXiv: https://arxiv.org/abs/2409.12789 · HTML: https://mlsanity.com/paper/2409.12789/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 2 of 2)

- [The Scenario Approach for Stochastic Model Predictive Control with Bounds on Closed-Loop Constraint Violations](https://mlsanity.com/paper/1307.5640/) (2013)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)

## Built on by (most cited first, 10 of 10)

- [Differentiable Nonlinear Model Predictive Control](https://mlsanity.com/paper/2505.01353/) (2025)
- [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)
- [Second-Order MPC-Based Distributed Q-Learning](https://mlsanity.com/paper/2511.16424/) (2025)
- [AI-Driven Controlled Environment Agriculture as Resilient Infrastructure for U.S. Fresh-Produce Supply Chains](https://mlsanity.com/paper/2605.23946/) (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 climate control is concerned with maximizing performance in terms of crop yield and resource efficiency. One promising approach is model predictive control (MPC), which leverages a model of the system to optimize the control inputs, while enforcing physical constraints. However, prediction models for greenhouse systems are inherently inaccurate due to the complexity of the real system and the uncertainty in predicted weather profiles. For model-based control approaches such as MPC, this can degrade performance and lead to constraint violations. Existing approaches address uncertainty in the prediction model with robust or stochastic MPC methodology; however, these necessarily reduce crop yield due to conservatism and often bear higher computational loads. In contrast, learning-based control approaches, such as reinforcement learning (RL), can handle uncertainty naturally by leveraging data to improve performance. This work proposes an MPC-based RL control framework to optimize the climate control performance in the presence of prediction uncertainty. The approach employs a parametrized MPC scheme that learns directly from data, in an online fashion, the parametrization of the constraints, prediction model, and optimization cost that minimizes constraint violations and maximizes climate control performance. Simulations show that the approach can learn an MPC controller that significantly outperforms the current state-of-the-art in terms of constraint violations and efficient crop growth.