An Open-AI gym environment for the Building Optimization Testing (BOPTEST) framework
Javier Arroyo, Carlo Manna, Fred Spiessens, Lieve Helsen
Abstract
The conventional controllers for building energy management have shown significant room for improvement, and disagree with the superb developments in state-of-the-art technologies like machine learning. This paper describes an OpenAI-Gym environment for the BOPTEST framework to rigorously benchmark different reinforcement learning algorithms among themselves and against other controllers (e.g. model predictive control) by building simulation. The design philosophy of the environment and its different features are introduced. Finally, the environment is demonstrated in one emulator building model to train a reinforcement learning algorithm and compare it against a classical control logic.