Learning Control for Air Conditioning Systems via Human Expressions
Qinglai Wei, Tao Li, Derong Liu
Abstract
In this article, a deep reinforcement learning method is developed to solve air conditioning control problems through human expressions. The main contribution of this article is to design a deep reinforcement learning method for air conditioning control problems with human expressions as the input for the first time. The method aims to eliminate human sleepiness and improve people's work efficiency as much as possible. First, the air conditioning system and deep reinforcement learning methods are introduced. Second, the image processing algorithm for human expressions is described. Third, the deep Q-network method is designed to obtain the optimal control policy for air conditioning systems. Finally, simulation results are given to illustrate the present method that can effectively eliminate sleepiness and improve the work environment of people.