mlsanityData to 14 Oct 2026

Federated Neuroevolution O-RAN: Enhancing the Robustness of Deep Reinforcement Learning xApps

arXiv 2506.12812 ↗First posted 15 Jun 2025cs.AI cs.NE cs.SYcited by 0

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

Abstract (arXiv)

The open radio access network (O-RAN) architecture introduces RAN intelligent controllers (RICs) to facilitate the management and optimization of the disaggregated RAN. Reinforcement learning (RL) and its advanced form, deep RL (DRL), are increasingly employed for designing intelligent controllers, or xApps, to be deployed in the near-real time (near-RT) RIC. These models often encounter local optima, which raise concerns about their reliability for RAN intelligent control. We therefore introduce Federated O-RAN enabled Neuroevolution (NE)-enhanced DRL (F-ONRL) that deploys an NE-based optimizer xApp in parallel to the RAN controller xApps. This NE-DRL xApp framework enables effective exploration and exploitation in the near-RT RIC without disrupting RAN operations. We implement the NE xApp along with a DRL xApp and deploy them on Open AI Cellular (OAIC) platform and present numerical results that demonstrate the improved robustness of xApps while effectively balancing the additional computational load.

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 8 of its 13 references that are in the map. First posted on arXiv 15 Jun 2025.

Builds on

8 of 13 references.

References · 8

  1. 2017Deep Reinforcement Learning: An Overviewcited by 333
  2. 2019Challenges of Real-World Reinforcement Learningcited by 291
  3. 2020Neuroevolution in Deep Neural Networks: Current Trends and Future Challengescited by 33
  4. 2021Toward Next Generation Open Radio Access Network--What O-RAN Can and Cannot Do!cited by 18
  5. 2021RLOps: Development Life-cycle of Reinforcement Learning Aided Open RANcited by 15
  6. 2022Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RANcited by 11
  7. 2022Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysiscited by 5
  8. 2023Intelligent O-RAN Traffic Steering for URLLC Through Deep Reinforcement Learningcited by 1

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  1. 2026Evolving Inspectable O-RAN Slicing xApps with LLMs
  2. 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
  3. 2025Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning
  4. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RAN
  5. 2026Demystifying Deep Reinforcement Learning: A Neuro-Symbolic Framework for Interpretable Open RAN Automation
  6. 2025Near-Real-Time Resource Slicing for QoS Optimization in 5G O-RAN using Deep Reinforcement Learning
  7. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  8. 2024Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges