Resumen
Due to the complex coupling of greenhouse environments, a number of challenges have been encountered in the research of automatic control in Venlo greenhouses. Most algorithms are only concerned with accuracy, yet energy-saving control is of great importance for improving economic benefits. Reinforcement learning, as an unsupervised machine learning method with a framework similar to that of feedback control, is a powerful tool for autonomous decision making in complex environments. However, the loss of benefits and increased time cost in the exploration process make it difficult to apply it to practical scenarios. This work proposes an energy-saving control algorithm for Venlo greenhouse skylights and wet curtain fan based on Reinforcement Learning with Soft Action Mask (SAM), which establishes a trainable SAM network with artificial rules to achieve sub-optimal policy initiation, safe exploration, and efficient optimization. Experiments in a simulated Venlo greenhouse model show that the approach, which is a feasible solution encoding human knowledge to improve the reinforcement learning process, can start with a safe, sub-optimal level and effectively and efficiently achieve reductions in the energy consumption, providing a suitable environment for crops and preventing frequent operation of the facility during the control process.