Event-triggered reinforcement learning; an application to buildings’ micro-climate control

Abstract

Smart buildings have great potential for shaping an energy-efficient, sustainable, and more economic future for our planet as buildings account for approximately 40% of the global energy consumption. However, most learning methods for micro-climate control in buildings are based on Markov Decision Processes with fixed transition times that suffer from high variance in the learning phase. Furthermore, ignoring its continuing-task nature the micro-climate control problem is often modeled and solved as an episodic-task problem with discounted rewards. This can result in a wrong optimization solution. To overcome these issues we propose an event-triggered learning control and formulate it based on Semi-Markov Decision Processes with variable transition times and in an average-reward setting. We show via simulation the efficacy of our approach in controlling the micro-climate of a single-zone building.