  Batch reinforcement learning (BRL) is an emerging research area in the RL
community. It learns exclusively from static datasets (i.e. replay buffers)
without interaction with the environment. In the offline settings, existing
replay experiences are used as prior knowledge for BRL models to find the
optimal policy. Thus, generating replay buffers is crucial for BRL model
benchmark. In our B2RL (Building Batch RL) dataset, we collected real-world
data from our building management systems, as well as buffers generated by
several behavioral policies in simulation environments. We believe it could
help building experts on BRL research. To the best of our knowledge, we are the
first to open-source building datasets for the purpose of BRL learning.
