Autonomous Building Control Using Offline Reinforcement Learning

Abstract

Artificial Intelligence (AI) powered building control allows deriving policies that are more flexible and energy efficient than standard control. However, there are challenges: environment interaction is used to train Reinforcement Learning (RL) agents but for building control it is often not possible to use a physical environment, and creating high fidelity simulators is a difficult task. With offline RL an agent can be trained without environment interaction, it is a data-driven approach to RL. In this paper, Conservative Q-Learning (CQL), an offline RL algorithm, is used to control the temperature setpoint in a room of a university campus building. The agent is trained using only the historical data available for this room. The results show that there is potential for offline RL in the field of building control, but also that there is room for improvement and need for further research in this area.