Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
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.
Key facts. Cited by 1 paper 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.
Builds on
19 of 55 references.
References · 19
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7 more
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- 2026HiGFRL: Hierarchical Graph Fusion-Driven Reinforcement Learning for Dependency-Aware Task Scheduling in Heterogeneous Cloud
- 2026Beyond Static Graph World Models: Learning Stochastic Latent Dynamics over Evolving Topologies
- 2026Topology-Aware Reinforcement Learning over Graphs for Resilient Power Distribution Networks
- 2026Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management
- 2025Topological Structure Learning Should Be A Research Priority for LLM-Based Multi-Agent Systems
- 2026Graph-GRPO: Stabilizing Multi-Agent Topology Learning via Group Relative Policy Optimization
- 2025An Attentive Graph Agent for Topology-Adaptive Cyber Defence