  Recent advancements in reinforcement learning algorithms have opened doors
for researchers to operate and optimize building energy management systems
autonomously. However, the lack of an easily configurable building dynamical
model and energy management task simulation and evaluation platform has
arguably slowed the progress in developing advanced and dedicated reinforcement
learning (RL) and control algorithms for building operation tasks. Here 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. In this paper, we discuss the design of this platform and compare
it with other existing building simulation frameworks. 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.
