  The rapid growth of machine learning (ML) has led to an increased demand for
computational power, resulting in larger data centers (DCs) and higher energy
consumption. To address this issue and reduce carbon emissions, intelligent
design and control of DC components such as IT servers, cabinets, HVAC cooling,
flexible load shifting, and battery energy storage are essential. However, the
complexity of designing and controlling them in tandem presents a significant
challenge. While some individual components like CFD-based design and
Reinforcement Learning (RL) based HVAC control have been researched, there's a
gap in the holistic design and optimization covering all elements
simultaneously. To tackle this, we've developed DCRL-Green, a multi-agent RL
environment that empowers the ML community to design data centers and research,
develop, and refine RL controllers for carbon footprint reduction in DCs. It is
a flexible, modular, scalable, and configurable platform that can handle large
High Performance Computing (HPC) clusters. Furthermore, in its default setup,
DCRL-Green provides a benchmark for evaluating single as well as multi-agent RL
algorithms. It easily allows users to subclass the default implementations and
design their own control approaches, encouraging community development for
sustainable data centers. Open Source Link:
https://github.com/HewlettPackard/dc-rl
