# Dual-Loop Control in DCVerse: Advancing Reliable Deployment of AI in Data Centers via Digital Twins

arXiv: https://arxiv.org/abs/2604.07559 · HTML: https://mlsanity.com/paper/2604.07559/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 10 of 10)

- [MOPO: Model-based Offline Policy Optimization](https://mlsanity.com/paper/2005.13239/) (2020)
- [MOReL : Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2005.05951/) (2020)
- [A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems](https://mlsanity.com/paper/2203.01387/) (2022)
- [Tianshou: a Highly Modularized Deep Reinforcement Learning Library](https://mlsanity.com/paper/2107.14171/) (2021)
- [Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.05077/) (2017)
- [Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges](https://mlsanity.com/paper/2406.07857/) (2024)
- [Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing](https://mlsanity.com/paper/2311.16876/) (2023)
- [Reinforcement Twinning: from digital twins to model-based reinforcement learning](https://mlsanity.com/paper/2311.03628/) (2023)
- [Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Control](https://mlsanity.com/paper/2012.06834/) (2020)
- [Data Center Cooling System Optimization Using Offline Reinforcement Learning](https://mlsanity.com/paper/2501.15085/) (2025)

## Abstract (arXiv)

The growing scale and complexity of modern data centers present major challenges in balancing energy efficiency with outage risk. Although Deep Reinforcement Learning (DRL) shows strong potential for intelligent control, its deployment in mission-critical systems is limited by data scarcity and the lack of real-time pre-evaluation mechanisms. This paper introduces the Dual-Loop Control Framework (DLCF), a digital twin-based architecture designed to overcome these challenges. The framework comprises three core entities: the physical system, a digital twin, and a policy reservoir of diverse DRL agents. These components interact through a dual-loop mechanism involving real-time data acquisition, data assimilation, DRL policy training, pre-evaluation, and expert verification. Theoretical analysis shows how DLCF can improve sample efficiency, generalization, safety, and optimality. Leveraging DLCF, we implemented the DCVerse platform and validated it through case studies on a real-world data center cooling system. The evaluation shows that our approach achieves up to 4.09% energy savings over conventional control strategies without violating SLA requirements. Additionally, the framework improves policy interpretability and supports more trustworthy DRL deployment. This work provides a foundation for reliable AI-based control in data centers and points toward future extensions for holistic, system-wide optimization.