Optimal control of greenhouse climate is one of the key techniques in digital agriculture.Greenhouse climate,a nonlinear and uncertain system,consists of several major environmental factors such as temperature,humidity,light intensity,and CO 2 concentration.Due to the complex coupled correlations,it is a challenge to achieve coordination control of greenhouse environmental factors.This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning.Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints.In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm,case-based reasoning （CBR） is seamlessly incorporated into the Q-learning process.The experimental results demonstrate that this approach is practical,highly effective and efficient.
