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

arXiv: https://arxiv.org/abs/2511.15002 · HTML: https://mlsanity.com/paper/2511.15002/

## Key facts

- Cited by 1 papers 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 (most cited first, 30 of 33)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Sharp Minima Can Generalize For Deep Nets](https://mlsanity.com/paper/1703.04933/) (2017)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [Understanding the impact of entropy on policy optimization](https://mlsanity.com/paper/1811.11214/) (2018)
- [A Study on Overfitting in Deep Reinforcement Learning](https://mlsanity.com/paper/1804.06893/) (2018)
- [Soft Actor-Critic for Discrete Action Settings](https://mlsanity.com/paper/1910.07207/) (2019)
- [Towards Understanding Sharpness-Aware Minimization](https://mlsanity.com/paper/2206.06232/) (2022)
- [Penalizing Gradient Norm for Efficiently Improving Generalization in Deep Learning](https://mlsanity.com/paper/2202.03599/) (2022)
- [Better Exploration with Optimistic Actor-Critic](https://mlsanity.com/paper/1910.12807/) (2019)
- [Relative Flatness and Generalization](https://mlsanity.com/paper/2001.00939/) (2020)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [PLASTIC: Improving Input and Label Plasticity for Sample Efficient Reinforcement Learning](https://mlsanity.com/paper/2306.10711/) (2023)
- [The Information Complexity of Learning Tasks, their Structure and their Distance](https://mlsanity.com/paper/1904.03292/) (2019)
- [TD-Regularized Actor-Critic Methods](https://mlsanity.com/paper/1812.08288/) (2018)
- [Federated Deep Reinforcement Learning for Resource Allocation in O-RAN Slicing](https://mlsanity.com/paper/2208.01736/) (2022)
- [Team Learning-Based Resource Allocation for Open Radio Access Network (O-RAN)](https://mlsanity.com/paper/2201.07385/) (2022)
- [Evolutionary Deep Reinforcement Learning for Dynamic Slice Management in O-RAN](https://mlsanity.com/paper/2208.14394/) (2022)
- [Intelligent Resource Slicing for eMBB and URLLC Coexistence in 5G and Beyond: A Deep Reinforcement Learning Based Approach](https://mlsanity.com/paper/2003.07651/) (2020)
- [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)
- [Attention-based Open RAN Slice Management using Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09490/) (2023)
- [A Comprehensive Tutorial and Survey of O-RAN: Exploring Slicing-aware Architecture, Deployment Options, Use Cases, and Challenges](https://mlsanity.com/paper/2405.03555/) (2024)
- [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)
- [Reinforcement Learning Based Resource Allocation for Network Slices in O-RAN Midhaul](https://mlsanity.com/paper/2211.07466/) (2022)
- [Boosting Exploration in Actor-Critic Algorithms by Incentivizing Plausible Novel States](https://mlsanity.com/paper/2210.00211/) (2022)
- [VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain](https://mlsanity.com/paper/2409.17469/) (2024)
- [Intelligent Task Offloading: Advanced MEC Task Offloading and Resource Management in 5G Networks](https://mlsanity.com/paper/2501.06242/) (2025)

## Built on by (most cited first, 1 of 1)

- [SAMPLe: SAM-based Optimizer for Prompt Learning in VLMs](https://mlsanity.com/paper/2607.05727/) (2026)

## 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.