RL-Guided MPC for Autonomous Greenhouse Control

TL;DR

This paper proposes an RL-Guided MPC framework for autonomous greenhouse control, combining reinforcement learning and model predictive control to improve efficiency and robustness in uncertain environments.

Contribution

It introduces a novel framework where RL guides MPC by providing terminal costs and constraints, enhancing control performance over traditional methods.

Findings

RL-Guided MPC outperforms standalone RL and MPC in simulations.

The approach is effective in both deterministic and uncertain environments.

Shorter prediction horizons are sufficient for high performance.

Abstract

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.