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

arXiv: https://arxiv.org/abs/2512.13715 · HTML: https://mlsanity.com/paper/2512.13715/

## 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.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 21 of 21)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms](https://mlsanity.com/paper/1908.10400/) (2019)
- [A Finite Time Analysis of Two Time-Scale Actor Critic Methods](https://mlsanity.com/paper/2005.01350/) (2020)
- [Deep Reinforcement Learning for Resource Management in Network Slicing](https://mlsanity.com/paper/1805.06591/) (2018)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [On the Specialization of FDRL Agents for Scalable and Distributed 6G RAN Slicing Orchestration](https://mlsanity.com/paper/2211.03088/) (2022)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN](https://mlsanity.com/paper/2410.03737/) (2024)
- [Intelligent Traffic Steering in Beyond 5G Open RAN based on LSTM Traffic Prediction](https://mlsanity.com/paper/2210.08829/) (2022)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAs](https://mlsanity.com/paper/2401.05042/) (2024)
- [Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2401.06922/) (2024)
- [Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing](https://mlsanity.com/paper/2109.07999/) (2021)
- [Federated Meta-Learning for Traffic Steering in O-RAN](https://mlsanity.com/paper/2209.05874/) (2022)
- [Knowledge Transfer and Reuse: A Case Study of AI-enabled Resource Management in RAN Slicing](https://mlsanity.com/paper/2212.09172/) (2022)
- [Meta Federated Reinforcement Learning for Distributed Resource Allocation](https://mlsanity.com/paper/2307.02900/) (2023)
- [On-Device Intelligence for 5G RAN: Knowledge Transfer and Federated Learning enabled UE-Centric Traffic Steering](https://mlsanity.com/paper/2304.11282/) (2023)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)
- [Hierarchical Reinforcement Learning Based Traffic Steering in Multi-RAT 5G Deployments](https://mlsanity.com/paper/2301.07818/) (2023)

## 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.