# LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers

arXiv: https://arxiv.org/abs/2511.00116 · HTML: https://mlsanity.com/paper/2511.00116/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use.
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 31 Oct 2025.

## Placed in

- LLM agents and tool use / [Reinforcement learning for LLM agents](https://mlsanity.com/agents/reinforcement-learning-for-llm-agents/)

## Builds on (most cited first, 11 of 11)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Instruction Tuning for Large Language Models: A Survey](https://mlsanity.com/paper/2308.10792/) (2023)
- [Verifiable Reinforcement Learning via Policy Extraction](https://mlsanity.com/paper/1805.08328/) (2018)
- [Benchmarking and Dissecting the Nvidia Hopper GPU Architecture](https://mlsanity.com/paper/2402.13499/) (2024)
- [A Machine Learning Framework for Real-time Inverse Modeling and Multi-objective Process Optimization of Composites for Active Manufacturing Control](https://mlsanity.com/paper/2104.11342/) (2021)
- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (2024)
- [PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability](https://mlsanity.com/paper/2310.03906/) (2023)
- [Carbon Footprint Reduction for Sustainable Data Centers in Real-Time](https://mlsanity.com/paper/2403.14092/) (2024)
- [A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale](https://mlsanity.com/paper/2410.05133/) (2024)
- [Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters](https://mlsanity.com/paper/2502.08337/) (2025)

## Built on by (most cited first, 3 of 3)

- [Fast 3D Surrogate Modeling for Data Center Thermal Management](https://mlsanity.com/paper/2511.11722/) (2025)
- [DiagnosticIQ: A Benchmark for LLM-Based Industrial Maintenance Action Recommendation from Symbolic Rules](https://mlsanity.com/paper/2605.08614/) (2026)
- [Artificial Intelligence for Energy Optimization in Data Centers](https://mlsanity.com/paper/2609.03716/) (2026)

## Abstract (arXiv)

Liquid cooling is critical for thermal management in high-density data centers with the rising AI workloads. However, machine learning-based controllers are essential to unlock greater energy efficiency and reliability, promoting sustainability. We present LC-Opt, a Sustainable Liquid Cooling (LC) benchmark environment, for reinforcement learning (RL) control strategies in energy-efficient liquid cooling of high-performance computing (HPC) systems. Built on the baseline of a high-fidelity digital twin of Oak Ridge National Lab's Frontier Supercomputer cooling system, LC-Opt provides detailed Modelica-based end-to-end models spanning site-level cooling towers to data center cabinets and server blade groups. RL agents optimize critical thermal controls like liquid supply temperature, flow rate, and granular valve actuation at the IT cabinet level, as well as cooling tower (CT) setpoints through a Gymnasium interface, with dynamic changes in workloads. This environment creates a multi-objective real-time optimization challenge balancing local thermal regulation and global energy efficiency, and also supports additional components like a heat recovery unit (HRU). We benchmark centralized and decentralized multi-agent RL approaches, demonstrate policy distillation into decision and regression trees for interpretable control, and explore LLM-based methods that explain control actions in natural language through an agentic mesh architecture designed to foster user trust and simplify system management. LC-Opt democratizes access to detailed, customizable liquid cooling models, enabling the ML community, operators, and vendors to develop sustainable data center liquid cooling control solutions.