Liquid Cooling Logic: When RL Keeps Its Cool

Modern AI and high-performance computing (HPC) are straining the physical limits of today’s data centers. As enterprises push toward ever-larger AI models and exascale workloads, the energy required to keep these systems cool has become one of the biggest—and often least visible—barriers to further growth. While liquid cooling has emerged as a promising alternative to traditional air-based cooling, most data centers still rely on static, rule-based control logic to determine how cooling systems behave. These rules were designed for simpler, lower-density environments, not multi-megawatt clusters packed with GPUs, CPUs, accelerators, heat-recovery systems, and tightly coupled thermal loops.

In practice, this gap means real-world operators are working with control strategies that can’t adjust quickly enough to shifting workloads, don’t account for the complex thermodynamics inside a modern liquid-cooled facility, and often waste substantial amounts of energy. Even organizations interested in applying advanced AI to optimize their cooling systems run into a fundamental obstacle: there has been no widely accepted, high-fidelity simulation environment where researchers or operators can safely develop and benchmark intelligent controllers before deploying them on mission-critical infrastructure. This is the central problem the research set out to solve.

The HPE researchers behind this work created LC-Opt, a high-resolution benchmark and simulation ecosystem designed specifically for liquid-cooled data centers. Rather than building a toy model or narrow testbed, they constructed LC-Opt as a comprehensive digital twin based on the architecture of major supercomputing systems, including full representations of cooling towers, heat exchangers, coolant loops, pumps, cabinet distribution units, blade groups, and even optional heat-recovery infrastructure. This ecosystem allows researchers to experiment with control strategies at multiple levels of a data-center thermal network—something existing benchmarks simply could not do.

To solve the optimization challenge itself, the researchers turned to a combination of reinforcement learning (RL) and “agentic AI,” creating a layered architecture of algorithms and intelligent agents that coordinate the entire cooling process. At the core are two coupled Markov Decision Processes (MDPs): one governs the behavior of cooling towers, and the other governs the thermal management of individual blade groups inside compute cabinets. They use multi-agent reinforcement learning, including multi-head versions of Proximal Policy Optimization (PPO), to learn control policies that minimize energy consumption while keeping temperatures within manufacturer-defined limits.

On top of these RL controllers, the team adds an LLM-driven agentic layer that distills the learned policies into large language models. These LLMs act as supervisory agents, explanation engines, and configuration managers—bridging the gap between opaque machine-learning policies and the operational transparency that real data-center engineers require. The result is not just an optimization engine, but a full decision-making framework capable of coordinating across components, adapting to changing conditions, and explaining its behavior in human-readable terms.

To understand how well the system performed, the researchers designed a sequence of experiments that progressively increased complexity—starting with small, controlled configurations and scaling up to full data-center representations. Each experiment placed the RL and agentic AI controllers into environments with realistic workloads, fluctuating thermal conditions, and operational constraints. The goal was not only to test whether the controllers could reduce energy consumption, but also whether they could maintain safe temperature margins when workloads shifted abruptly or when cooling equipment had to respond to competing demands.

The experiments compared multiple control strategies: traditional rule-based logic, isolated RL agents acting on individual components, and multi-agent RL systems coordinating across cooling towers and compute cabinets. They also introduced variations in system size, adding more cabinets, coolant loops, and cooling towers to evaluate whether learned control strategies could generalize rather than overfit to a specific layout. By designing the experiments this way, the researchers were testing a broader question: could intelligent controllers scale and adapt the way real-world data centers must, without requiring a complete redesign each time hardware grows?

A key part of the experimentation also involved testing the agentic AI layer—specifically, how well the distilled language models reproduced or improved upon the underlying RL behavior. These models were evaluated on their ability to make consistent decisions across scenarios, provide rationales that aligned with the actual physical state of the system, and maintain operational safety. This was particularly important because interpretability is a non-negotiable requirement for data-center operations; opaque or unpredictable behavior is simply not deployable.

To determine whether the system succeeded or failed in each scenario, the researchers created a set of operationally relevant criteria. One of the primary indicators was thermal compliance—whether the controllers could keep compute components within acceptable temperature thresholds across varying loads. Another key measure was energy efficiency across the cooling network, specifically whether the system reduced unnecessary power consumption while still meeting thermal demands. These criteria reflect the real tradeoffs operators manage every day: reliable temperatures, minimal risk of overheating, and efficient use of power and water.

The evaluation framework also looked at system stability and responsiveness. Controllers were judged on how smoothly they handled transitions—such as sudden increases in compute activity or rapid changes in environmental conditions—without causing thermal spikes or excessive oscillation in cooling behavior. Additionally, human factors played a role: the clarity and utility of the explanations generated by the LLM-based agents influenced how the team assessed readiness for potential operational use. In essence, the experiments were designed not just to test raw performance but to evaluate whether the system behaved in a way that a real-world operator could trust, understand, and oversee.

A central part of judging whether this approach was viable involved assessing not only what the controllers achieved, but how they behaved while doing so. Success required far more than demonstrating that an algorithm could optimize a few metrics in isolation. The researchers evaluated the system’s decision-making patterns, its consistency under stress, and its ability to balance competing operational goals without destabilizing the broader thermal environment. They examined whether the controllers responded proportionally to developing conditions, adjusted smoothly rather than erratically, and maintained a level of predictability that human operators could easily interpret. Equally important was the system’s capacity to avoid risky edge cases—actions that technically optimize energy usage but inadvertently push components closer to unsafe thermal states. This emphasis on behavioral quality underscored the team’s goal of building controllers that operate like disciplined, reliable co-pilots rather than opaque automation.

Interpretability became another dimension of evaluation. The LLM-based supervisory layer was expected to produce explanations that accurately reflected the underlying state of the cooling network and the logic behind particular control decisions. These explanations were reviewed not for stylistic polish, but for whether they revealed genuine alignment between the model’s internal reasoning and the physical principles governing data-center cooling. A clear, truthful explanation was considered a signal that the system could earn operator trust—an essential criterion for any technology that hopes to manage mission-critical infrastructure.

While the research established a strong foundation, the team acknowledged several limitations that shape future work. The current simulation focuses on liquid-cooled architectures and does not yet incorporate environments where air and liquid cooling coexist—a common configuration during transitional phases of data-center modernization. It also abstracts away chip-level thermal behavior, which is becoming increasingly important as processor power density rises. Broadening the model to incorporate more diverse climates, edge deployments, or unconventional heat-recovery schemes would improve its ability to generalize across global facilities.

Looking ahead, the researchers see opportunities to strengthen real-world readiness through tighter integration between simulation and live telemetry streams, enabling continuous validation and adaptation. The framework may also serve as a proving ground for hybrid controllers that combine RL policies with physics-informed models or safety-constrained optimization layers.

The broader impact of this work is straightforward yet significant: it transforms how organizations might design, test, and deploy intelligent cooling systems. By providing a rigorous environment for experimentation, the benchmark accelerates innovation while reducing operational risk. For industries grappling with skyrocketing thermal loads and sustainability pressures, this approach offers a way to unlock higher compute density without proportionally increasing energy consumption. Ultimately, the solution reframes data-center cooling from a fixed constraint into a dynamic optimization problem—one that intelligent systems can learn to manage with increasing sophistication and transparency.