# RL-Guided MPC for Autonomous Greenhouse Control

arXiv: https://arxiv.org/abs/2506.13278 · HTML: https://mlsanity.com/paper/2506.13278/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Improving greenhouse fruit-production control by integrating reinforcement learning into short-horizon model predictive control](https://mlsanity.com/paper/2607.07365/) (2026)

## Abstract (arXiv)

The efficient operation of greenhouses is essential for enhancing crop yield while minimizing energy costs. This paper investigates a control strategy that integrates Reinforcement Learning (RL) and Model Predictive Control (MPC) to optimize economic benefits in autonomous greenhouses. Previous research has explored the use of RL and MPC for greenhouse control individually, or by using MPC as the function approximator for the RL agent. This study introduces the RL-Guided MPC framework, where a RL policy is trained and then used to construct a terminal cost and terminal region constraint for the MPC optimization problem. This approach leverages the ability to handle uncertainties of RL with MPC's online optimization to improve overall control performance. The RL-Guided MPC framework is compared with both MPC and RL via numerical simulations. Two scenarios are considered: a deterministic environment and an uncertain environment. Simulation results demonstrate that, in both environments, RL-Guided MPC outperforms both RL and MPC with shorter prediction horizons.