# Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks

arXiv: https://arxiv.org/abs/2509.04973 · HTML: https://mlsanity.com/paper/2509.04973/

## Key facts

- Cited by 1 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 19 of its 55 references that are in the map.
- First posted on arXiv 5 Sep 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, 19 of 19)

- [Evaluating Explainability for Graph Neural Networks](https://mlsanity.com/paper/2208.09339/) (2022)
- [CFR-RL: Traffic Engineering with Reinforcement Learning in SDN](https://mlsanity.com/paper/2004.11986/) (2020)
- [GDDR: GNN-based Data-Driven Routing](https://mlsanity.com/paper/2104.09919/) (2021)
- [Structured Memory Mechanisms for Stable Context Representation in Large Language Models](https://mlsanity.com/paper/2505.22921/) (2025)
- [Packet Routing with Graph Attention Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2107.13181/) (2021)
- [A Deep Learning Framework for Sequence Mining with Bidirectional LSTM and Multi-Scale Attention](https://mlsanity.com/paper/2504.15223/) (2025)
- [Structure-Aware Temporal Modeling for Chronic Disease Progression Prediction](https://mlsanity.com/paper/2508.14942/) (2025)
- [DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks](https://mlsanity.com/paper/2111.15013/) (2021)
- [Artificial Intelligence-Based Multiscale Temporal Modeling for Anomaly Detection in Cloud Services](https://mlsanity.com/paper/2508.14503/) (2025)
- [Semantic and Structural Analysis of Implicit Biases in Large Language Models: An Interpretable Approach](https://mlsanity.com/paper/2508.06155/) (2025)
- [Knowledge Graph-Infused Fine-Tuning for Structured Reasoning in Large Language Models](https://mlsanity.com/paper/2508.14427/) (2025)
- [Deep Skin Lesion Segmentation with Transformer-CNN Fusion: Toward Intelligent Skin Cancer Analysis](https://mlsanity.com/paper/2508.14509/) (2025)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [Pre-trained Language Models and Few-shot Learning for Medical Entity Extraction](https://mlsanity.com/paper/2504.04385/) (2025)
- [Application of Deep Generative Models for Anomaly Detection in Complex Financial Transactions](https://mlsanity.com/paper/2504.15491/) (2025)
- [Graph Neural Network-Based Collaborative Perception for Adaptive Scheduling in Distributed Systems](https://mlsanity.com/paper/2505.16248/) (2025)
- [Behavioral Anomaly Detection in Distributed Systems via Federated Contrastive Learning](https://mlsanity.com/paper/2506.19246/) (2025)
- [Graph Neural Network and Transformer Integration for Unsupervised System Anomaly Discovery](https://mlsanity.com/paper/2508.09401/) (2025)
- [Domain-Adversarial Transfer Learning for Fault Root Cause Identification in Cloud Computing Systems](https://mlsanity.com/paper/2507.02233/) (2025)

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

- [Dynamic Topic Evolution with Temporal Decay and Attention in Large Language Models](https://mlsanity.com/paper/2510.10613/) (2025)

## Abstract (arXiv)

This paper proposes a topology-aware graph reinforcement learning approach to address the routing policy optimization problem in cloud server environments. The method builds a unified framework for state representation and structural evolution by integrating a Structure-Aware State Encoding (SASE) module and a Policy-Adaptive Graph Update (PAGU) mechanism. It aims to tackle the challenges of decision instability and insufficient structural awareness under dynamic topologies. The SASE module models node states through multi-layer graph convolution and structural positional embeddings, capturing high-order dependencies in the communication topology and enhancing the expressiveness of state representations. The PAGU module adjusts the graph structure based on policy behavior shifts and reward feedback, enabling adaptive structural updates in dynamic environments. Experiments are conducted on the real-world GEANT topology dataset, where the model is systematically evaluated against several representative baselines in terms of throughput, latency control, and link balance. Additional experiments, including hyperparameter sensitivity, graph sparsity perturbation, and node feature dimensionality variation, further explore the impact of structure modeling and graph updates on model stability and decision quality. Results show that the proposed method outperforms existing graph reinforcement learning models across multiple performance metrics, achieving efficient and robust routing in dynamic and complex cloud networks.