mlsanityData to 14 Oct 2026

Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN

arXiv 2512.13715 ↗First posted 8 Dec 2025cs.AI cs.LG cs.SYcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

The increasing complexity of modern applications demands wireless networks capable of real time adaptability and efficient resource management. The Open Radio Access Network (O-RAN) architecture, with its RAN Intelligent Controller (RIC) modules, has emerged as a pivotal solution for dynamic resource management and network slicing. While artificial intelligence (AI) driven methods have shown promise, most approaches struggle to maintain performance under unpredictable and highly dynamic conditions. This paper proposes an adaptive Meta Hierarchical Reinforcement Learning (Meta-HRL) framework, inspired by Model Agnostic Meta Learning (MAML), to jointly optimize resource allocation and network slicing in O-RAN. The framework integrates hierarchical control with meta learning to enable both global and local adaptation: the high-level controller allocates resources across slices, while low level agents perform intra slice scheduling. The adaptive meta-update mechanism weights tasks by temporal difference error variance, improving stability and prioritizing complex network scenarios. Theoretical analysis establishes sublinear convergence and regret guarantees for the two-level learning process. Simulation results demonstrate a 19.8% improvement in network management efficiency compared with baseline RL and meta-RL approaches, along with faster adaptation and higher QoS satisfaction across eMBB, URLLC, and mMTC slices. Additional ablation and scalability studies confirm the method's robustness, achieving up to 40% faster adaptation and consistent fairness, latency, and throughput performance as network scale increases.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 21 of its 33 references that are in the map. First posted on arXiv 8 Dec 2025.

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21 of 33 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN30 Sep
  2. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning12 Jan

Other references · 19

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,897
  2. 2019On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithmscited by 132
  3. 2020A Finite Time Analysis of Two Time-Scale Actor Critic Methodscited by 122
  4. 2018Deep Reinforcement Learning for Resource Management in Network Slicingcited by 27
  5. 2022dApps: Distributed Applications for Real-time Inference and Control in O-RANcited by 17
  6. 2022Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicingcited by 14
  7. 2022Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RANcited by 11
  8. 2022On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestrationcited by 10
  9. 2022Predictive Closed-Loop Service Automation in O-RAN based Network Slicingcited by 8
  10. 2022Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Predictioncited by 7
  11. 2023Attention-based Open RAN Slice Management using Deep Reinforcement Learningcited by 7
  12. 2024DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAscited by 5
7 more
  1. 2021Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicingcited by 4
  2. 2022Federated Meta-Learning for Traffic Steering in O-RANcited by 4
  3. 2022Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicingcited by 4
  4. 2023Meta Federated Reinforcement Learning for Distributed Resource Allocationcited by 4
  5. 2023On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steeringcited by 3
  6. 2025Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networkscited by 3
  7. 2023Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deploymentscited by 1

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  1. 2023Advancing RAN Slicing with Offline Reinforcement Learning
  2. 2025Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
  3. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing
  4. 2025Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning
  5. 2025Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
  6. 2024AdapShare: An RL-Based Dynamic Spectrum Sharing Solution for O-RAN
  7. 2025QoS-Aware Dynamic CU Selection in O-RAN with Graph-Based Reinforcement Learning
  8. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications