B2RL: An open-source Dataset for Building Batch Reinforcement Learning
Published in RLEM Workshop (BuildSys), 2022
Recommended citation: Liu, Hsin-Yu, Xiaohan Fu, Bharathan Balaji, Rajesh Gupta, and Dezhi Hong. "B2RL: an open-source dataset for building batch reinforcement learning." In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, pp. 462-465. 2022. https://arxiv.org/abs/2209.15626
Link to conference presentation video
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.