# Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN

arXiv: https://arxiv.org/abs/2410.03737 · HTML: https://mlsanity.com/paper/2410.03737/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 21 references that are in the map.
- First posted on arXiv 30 Sep 2024.

## 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, 13 of 13)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [A Tutorial on Meta-Reinforcement Learning](https://mlsanity.com/paper/2301.08028/) (2023)
- [Understanding O-RAN: Architecture, Interfaces, Algorithms, Security, and Research Challenges](https://mlsanity.com/paper/2202.01032/) (2022)
- [dApps: Distributed Applications for Real-time Inference and Control in O-RAN](https://mlsanity.com/paper/2203.02370/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [Predictive Closed-Loop Service Automation in O-RAN based Network Slicing](https://mlsanity.com/paper/2202.01966/) (2022)
- [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)
- [Federated Meta-Learning for Traffic Steering in O-RAN](https://mlsanity.com/paper/2209.05874/) (2022)
- [Meta Federated Reinforcement Learning for Distributed Resource Allocation](https://mlsanity.com/paper/2307.02900/) (2023)
- [Adaptive Resource Allocation for Virtualized Base Stations in O-RAN with Online Learning](https://mlsanity.com/paper/2309.01730/) (2023)

## Built on by (most cited first, 8 of 8)

- [Multi-Agent Reinforcement Learning for Resources Allocation Optimization: A Survey](https://mlsanity.com/paper/2504.21048/) (2025)
- [ML-Enabled Open RAN: A Comprehensive Survey of Architectures, Challenges, and Opportunities](https://mlsanity.com/paper/2604.01239/) (2026)
- [ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicing](https://mlsanity.com/paper/2506.00576/) (2025)
- [Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicing](https://mlsanity.com/paper/2506.00574/) (2025)
- [Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning](https://mlsanity.com/paper/2511.15002/) (2025)
- [Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN](https://mlsanity.com/paper/2512.13715/) (2025)
- [ORAN-DEFEND: Subspace Detection and Sanitization of Backdoor DRL xApps in Open RAN](https://mlsanity.com/paper/2607.06647/) (2026)
- [Place, Slice and Schedule: Hierarchical O-RAN Control of a Tethered mmWave UAV-gNB](https://mlsanity.com/paper/2608.23824/) (2026)

## Abstract (arXiv)

As wireless networks grow to support more complex applications, the Open Radio Access Network (O-RAN) architecture, with its smart RAN Intelligent Controller (RIC) modules, becomes a crucial solution for real-time network data collection, analysis, and dynamic management of network resources including radio resource blocks and downlink power allocation. Utilizing artificial intelligence (AI) and machine learning (ML), O-RAN addresses the variable demands of modern networks with unprecedented efficiency and adaptability. Despite progress in using ML-based strategies for network optimization, challenges remain, particularly in the dynamic allocation of resources in unpredictable environments. This paper proposes a novel Meta Deep Reinforcement Learning (Meta-DRL) strategy, inspired by Model-Agnostic Meta-Learning (MAML), to advance resource block and downlink power allocation in O-RAN. Our approach leverages O-RAN's disaggregated architecture with virtual distributed units (DUs) and meta-DRL strategies, enabling adaptive and localized decision-making that significantly enhances network efficiency. By integrating meta-learning, our system quickly adapts to new network conditions, optimizing resource allocation in real-time. This results in a 19.8% improvement in network management performance over traditional methods, advancing the capabilities of next-generation wireless networks.