# PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability

arXiv: https://arxiv.org/abs/2310.03906 · HTML: https://mlsanity.com/paper/2310.03906/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 5 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [AI and ML Accelerator Survey and Trends](https://mlsanity.com/paper/2210.04055/) (2022)
- [Reinforcement Learning Testbed for Power-Consumption Optimization](https://mlsanity.com/paper/1808.10427/) (2018)
- [Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters](https://mlsanity.com/paper/2209.05656/) (2022)
- [ModelicaGym: Applying Reinforcement Learning to Modelica Models](https://mlsanity.com/paper/1909.08604/) (2019)

## Built on by (most cited first, 6 of 6)

- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (2024)
- [SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learning](https://mlsanity.com/paper/2412.08293/) (2024)
- [Carbon Footprint Reduction for Sustainable Data Centers in Real-Time](https://mlsanity.com/paper/2403.14092/) (2024)
- [LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers](https://mlsanity.com/paper/2511.00116/) (2025)
- [Sustainability of Data Center Digital Twins with Reinforcement Learning](https://mlsanity.com/paper/2404.10786/) (2024)
- [OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination](https://mlsanity.com/paper/2605.05519/) (2026)

## Abstract (arXiv)

The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such as cooling and IT energy usage. A significant challenge in this pursuit is the lack of a configurable and scalable thermal data center model that offers an end-to-end pipeline. Data centers consist of multiple IT components whose geometric configuration and heat dissipation make thermal modeling difficult. This paper presents PyDCM, a customizable Data Center Model implemented in Python, that allows users to create unique configurations of IT equipment with custom server specifications and geometric arrangements of IT cabinets. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations and scales sublinearly with the number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via the Gymnasium wrapper to optimize data center cooling and offers a user-friendly platform for testing various data center design prototypes.