One challenge of building research is the lack of a benchmark simulation environment for developing and evaluating different RL algorithms with realistic building models. In a recent work, we propose “BEAR”, a physics-principled Building Environment for Control and Reinforcement Learning. The platform allows researchers to benchmark both model-based and model-free controllers using a broad collection of standard building models in Python without co-simulation using external building simulators. We demonstrate the compatibility and performance of BEAR with different controllers, including both model predictive control (MPC) and several state-of-the-art RL methods with two case studies.

BEAR is available at https://github.com/chz056/BEAR