Chinese Journal of Intelligent Science and Technology ›› 2020, Vol. 2 ›› Issue (4): 348-353.doi: 10.11959/j.issn.2096-6652.202037

• Special Issue: Deep Reinforcement Learning • Previous Articles     Next Articles

Intelligent heating temperature control system based on deep reinforcement learning

Tao LI1,2, Qinglai WEI1,2   

  1. 1 The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Science, Beijing 100190, China
    2 University of Chinese Academy of Sciences, Beijing 100049, China
  • Revised:2020-11-30 Online:2020-12-15 Published:2020-12-01
  • Supported by:
    The National Natural Science Foundation of China(61722312);The National Natural Science Foundation of China(62073321)


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.

Key words: deep reinforcement learning, heating equipment, temperature control, fatigue detection, image processing

CLC Number: 

No Suggested Reading articles found!