mlsanityData to 14 Oct 2026

Task Specific Sharpness Aware O-RAN Resource Management using Multi Agent Reinforcement Learning

arXiv 2511.15002 ↗First posted 19 Nov 2025cs.AI cs.LGcited by 1

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Abstract (arXiv)

Next-generation networks utilize the Open Radio Access Network (O-RAN) architecture to enable dynamic resource management, facilitated by the RAN Intelligent Controller (RIC). While deep reinforcement learning (DRL) models show promise in optimizing network resources, they often struggle with robustness and generalizability in dynamic environments. This paper introduces a novel resource management approach that enhances the Soft Actor Critic (SAC) algorithm with Sharpness-Aware Minimization (SAM) in a distributed Multi-Agent RL (MARL) framework. Our method introduces an adaptive and selective SAM mechanism, where regularization is explicitly driven by temporal-difference (TD)-error variance, ensuring that only agents facing high environmental complexity are regularized. This targeted strategy reduces unnecessary overhead, improves training stability, and enhances generalization without sacrificing learning efficiency. We further incorporate a dynamic ρ scheduling scheme to refine the exploration-exploitation trade-off across agents. Experimental results show our method significantly outperforms conventional DRL approaches, yielding up to a 22% improvement in resource allocation efficiency and ensuring superior QoS satisfaction across diverse O-RAN slices.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 33 of its 52 references that are in the map. First posted on arXiv 19 Nov 2025.

Builds on

33 of 52 references.

References · 33

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2020Sharpness-Aware Minimization for Efficiently Improving Generalizationcited by 1,417
  3. 2017Sharp Minima Can Generalize For Deep Netscited by 466
  4. 2017Deep Reinforcement Learning: An Overviewcited by 333
  5. 2018Understanding the impact of entropy on policy optimizationcited by 176
  6. 2018A Study on Overfitting in Deep Reinforcement Learningcited by 174
  7. 2019Soft Actor-Critic for Discrete Action Settingscited by 174
  8. 2022Towards Understanding Sharpness-Aware Minimizationcited by 146
  9. 2022Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learningcited by 122
  10. 2019Better Exploration with Optimistic Actor-Criticcited by 88
  11. 2020Relative Flatness and Generalizationcited by 88
  12. 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
21 more
  1. 2023PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learningcited by 42
  2. 2019The Information Complexity of Learning Tasks, their Structure and their Distancecited by 36
  3. 2018TD-Regularized Actor-Critic Methodscited by 14
  4. 2022Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicingcited by 14
  5. 2022Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)cited by 11
  6. 2022Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RANcited by 11
  7. 2020Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approachcited by 10
  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. 2024Meta Reinforcement Learning Approach for Adaptive Resource Optimization in O-RANcited by 8
  11. 2023Attention-based Open RAN Slice Management using Deep Reinforcement Learningcited by 7
  12. 2024A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challengescited by 7
  13. 2024DRL-based Latency-Aware Network Slicing in O-RAN with Time-Varying SLAscited by 5
  14. 2024Open RAN LSTM Traffic Prediction and Slice Management using Deep Reinforcement Learningcited by 5
  15. 2022Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaulcited by 4
  16. 2022Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel Statescited by 3
  17. 2024VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terraincited by 3
  18. 2025Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networkscited by 3
  19. 2025ORAN-GUIDE: RAG-Driven Prompt Learning for LLM-Augmented Reinforcement Learning in O-RAN Network Slicingcited by 3
  20. 2022A Collaborative Statistical Actor-Critic Learning Approach for 6G Network Slicing Controlcited by 2
  21. 2025Prompt-Tuned LLM-Augmented DRL for Dynamic O-RAN Network Slicingcited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026SAMPLe: SAM-based Optimizer for Prompt Learning in VLMscited by 0

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  1. 2025Meta Hierarchical Reinforcement Learning for Scalable Resource Management in O-RAN
  2. 2026Adaptive RAN Slicing Control via Reward-Free Self-Finetuning Agents
  3. 2024Dynamics of Resource Allocation in O-RANs: An In-depth Exploration of On-Policy and Off-Policy Deep Reinforcement Learning for Real-Time Applications
  4. 2024Towards Practical Operation of Deep Reinforcement Learning Agents in Real-World Network Management at Open RAN Edges
  5. 2025An Offline Multi-Agent Reinforcement Learning Framework for Radio Resource Management
  6. 2025A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services
  7. 2025Generalization in Reinforcement Learning for Radio Access Networks
  8. 2025Percentile-Based Deep Reinforcement Learning and Reward Based Personalization For Delay Aware RAN Slicing in O-RAN