MB2C
Xianzhong Ding, Wan Du, Alberto Cerpa
Abstract
Reinforcement learning has been widely studied for controlling Heating, Ventilation, and Air conditioning (HVAC) systems. Most of the existing works are focused on Model-Free Reinforcement Learning (MFRL), which learns an agent by extensively trial-and-error interaction with a real building. However, one of the fundamental problems with MFRL is the very large amount of training data required to converge to acceptable performance. Although simulation models have been used to generate sufficient training data to accelerate the training process, MFRL needs a high-fidelity building model for simulation, which is also hard to calibrate. As a result, Model-Based Reinforcement Learning (MBRL) has been used for HVAC control. While MBRL schemes can achieve excellent sample efficiency (i.e. less training data), they often lag behind model-free approaches in terms of asymptotic control performance (i.e. high energy savings while meeting occupants' thermal comfort).