  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.
