mlsanityData to 14 Oct 2026

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

arXiv 2604.07559 ↗First posted 8 Apr 2026cs.AIcited by 0

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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.

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References · 10

  1. 2020MOPO: Model-based Offline Policy Optimizationcited by 636
  2. 2020MOReL : Model-Based Offline Reinforcement Learningcited by 590
  3. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problemscited by 231
  4. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  5. 2017Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learningcited by 18
  6. 2024Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challengescited by 10
  7. 2023Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicingcited by 7
  8. 2023Reinforcement Twinning: from digital twins to model-based reinforcement learningcited by 5
  9. 2020Deep Reinforcement Learning for Tropical Air Free-Cooled Data Center Controlcited by 4
  10. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learningcited by 4

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  1. 2026DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations
  2. 2025Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
  3. 2024SustainDC: Benchmarking for Sustainable Data Center Control
  4. 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
  5. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
  6. 2024Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
  7. 2024Constructing and Evaluating Digital Twins: An Intelligent Framework for DT Development
  8. 2025LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers