# Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control

arXiv: https://arxiv.org/abs/2607.07365 · HTML: https://mlsanity.com/paper/2607.07365/

## 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 52 references that are in the map.
- First posted on arXiv 8 Jul 2026.

## 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, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [A Survey of Zero-shot Generalisation in Deep Reinforcement Learning](https://mlsanity.com/paper/2111.09794/) (2021)
- [A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning](https://mlsanity.com/paper/1806.07937/) (2018)
- [The Generalization Gap in Offline Reinforcement Learning](https://mlsanity.com/paper/2312.05742/) (2023)
- [Reinforcement Learning Versus Model Predictive Control on Greenhouse Climate Control](https://mlsanity.com/paper/2303.06110/) (2023)
- [Reinforcement Learning-based Model Predictive Control for Greenhouse Climate Control](https://mlsanity.com/paper/2409.12789/) (2024)
- [GreenLight-Gym: Reinforcement learning benchmark environment for control of greenhouse production systems](https://mlsanity.com/paper/2410.05336/) (2024)
- [Rollout Then Optimize: A One-Step Newton Refinement of Learned Policies for Nonlinear Model Predictive Control](https://mlsanity.com/paper/2504.02710/) (2025)
- [Comparison of Dynamic Tomato Growth Models for Optimal Control in Greenhouses](https://mlsanity.com/paper/2308.06031/) (2023)
- [RL-Guided MPC for Autonomous Greenhouse Control](https://mlsanity.com/paper/2506.13278/) (2025)

## Abstract (arXiv)

Greenhouse fruit-production control aims to maximize the economic performance (fruit revenue minus operating costs) while operating within system constraints under external weather disturbances. Control methods need to balance the delayed economic benefit of fruit yield with current operating costs. For such problems, model predictive control (MPC) can explicitly handle system constraints under future weather disturbances, but can become computationally demanding when using sufficiently long prediction horizons for (relatively large) nonlinear greenhouse fruit production models. In contrast, reinforcement learning (RL) can learn control policies offline while considering longer-term economic performance, but struggles to enforce system constraints, and performance may degrade under unseen weather trajectories. This work proposes trajectory-selection RL-MPC, a framework that incorporates longer-term economic information of fruit yield into a short-horizon MPC optimization problem. The framework uses an RL rollout trajectory to define a terminal region constraint and terminal cost. Next, a nonlinear MPC solves a short-horizon optimization problem with these terminal ingredients to find a local optimum. Finally, the framework selects and executes the first input from the trajectory with the better objective value, either from the MPC-predicted or the RL rollout trajectory. The method is applied to GreenLight, a large-scale greenhouse tomato production model that exhibits stiff dynamics. The simulation results show that trajectory-selection RL-MPC with a one-hour prediction horizon matches the closed-loop performance of a high-performing guiding policy while significantly improving over standalone MPC with the same horizon.