Reinforcement learning (RL) was first demonstrated to be a feasible approach to controlling heating, ventilation, and air conditioning (HVAC) systems more than a decade ago. However, there has been limited progress towards a practical and scalable RL solution for HVAC control. While one can train an RL agent in simulation, it is not cost-effective to create a model for each thermal zone or building. Likewise, existing RL agents generally take a long time to learn and are opaque to expert interrogation, making them unattractive for real-world deployment.
To tackle these challenges, Gnu-RL was developed by Mario Bergés, a professor of civil and environmental engineering at Carnegie Mellon University, College of Engineering, collaborated with Ph.D. student Bingqing Chen. Gnu-RL is a novel approach that enables practical deployment of RL for HVAC control and requires no prior information other than historical data from existing HVAC controllers. You can read about it here.
Bergés and Chen presented a paper on Gnu-RL at the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys 2019). The conference took place in New York City on November 13 and 14. More on that here.
