Intelligent heating temperature control system based on deep reinforcement learning - Tao LI; Qinglai WEI (智能科学与技术学报)
It is of great significance to study how to adjust the room temperature adaptively through heating equipment to improve the comfort of the indoor environment.Therefore, a double deep Q network method was developed to control the valve opening of heating equipment to adjust the indoor temperature in real time via human expressions.Firstly, the preprocessing algorithm for the original input state was introduced.Secondly, a double deep Q network method was designed to learn the optimal control policy of the valve opening of heating equipment.Finally, simulation results were given to illustrate the effectiveness of the method proposed.