B2RL: An open-source Dataset for Building Batch Reinforcement Learning

TL;DR

This paper introduces B2RL, an open-source dataset from building management systems and simulated policies, aimed at advancing batch reinforcement learning research in real-world building control scenarios.

Contribution

It provides the first open-source building dataset specifically designed for batch reinforcement learning, combining real-world and simulated data for benchmarking.

Findings

First open-source building dataset for BRL.

Includes real-world and simulated policy data.

Aims to facilitate BRL research in building management.

Abstract

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.