# Deep Reinforcement Learning meets Graph Neural Networks: exploring a routing optimization use case

arXiv: https://arxiv.org/abs/1910.07421 · HTML: https://mlsanity.com/paper/1910.07421/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2019.

## Built on by (most cited first, 30 of 53)

- [Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks](https://mlsanity.com/paper/2104.07886/) (2021)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization](https://mlsanity.com/paper/2211.03074/) (2022)
- [Unveiling the potential of Graph Neural Networks for robust Intrusion Detection](https://mlsanity.com/paper/2107.14756/) (2021)
- [Resilient Routing: Risk-Aware Dynamic Routing in Smart Logistics via Spatiotemporal Graph Learning](https://mlsanity.com/paper/2601.13632/) (2026)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [LaDe: The First Comprehensive Last-mile Delivery Dataset from Industry](https://mlsanity.com/paper/2306.10675/) (2023)
- [Graph Computing for Financial Crime and Fraud Detection: Trends, Challenges and Outlook](https://mlsanity.com/paper/2103.03227/) (2021)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [LeNSE: Learning To Navigate Subgraph Embeddings for Large-Scale Combinatorial Optimisation](https://mlsanity.com/paper/2205.10106/) (2022)
- [Towards Generalizability of Multi-Agent Reinforcement Learning in Graphs with Recurrent Message Passing](https://mlsanity.com/paper/2402.05027/) (2024)
- [Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agents](https://mlsanity.com/paper/2509.16151/) (2025)
- [The Graph Neural Networking Challenge: A Worldwide Competition for Education in AI/ML for Networks](https://mlsanity.com/paper/2107.12433/) (2021)
- [A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems](https://mlsanity.com/paper/2109.12073/) (2021)
- [Optimizing Tensor Network Contraction Using Reinforcement Learning](https://mlsanity.com/paper/2204.09052/) (2022)
- [Networked Restless Bandits with Positive Externalities](https://mlsanity.com/paper/2212.05144/) (2022)
- [Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligence](https://mlsanity.com/paper/2309.10186/) (2023)
- [Causal Question Answering with Reinforcement Learning](https://mlsanity.com/paper/2311.02760/) (2023)
- [Financial Crime & Fraud Detection Using Graph Computing: Application Considerations & Outlook](https://mlsanity.com/paper/2103.01854/) (2021)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [The Power of Communication in a Distributed Multi-Agent System](https://mlsanity.com/paper/2111.15611/) (2021)
- [RL4ReAl: Reinforcement Learning for Register Allocation](https://mlsanity.com/paper/2204.02013/) (2022)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [A Graph Attention Learning Approach to Antenna Tilt Optimization](https://mlsanity.com/paper/2112.14843/) (2021)
- [Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution](https://mlsanity.com/paper/2204.11350/) (2022)
- [Learning an Adaptive Forwarding Strategy for Mobile Wireless Networks: Resource Usage vs. Latency](https://mlsanity.com/paper/2207.11386/) (2022)
- [FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix Multiplications on Tensor Cores](https://mlsanity.com/paper/2412.11007/) (2024)
- [Constraint-Aware Route Recommendation from Natural Language via Hierarchical LLM Agents](https://mlsanity.com/paper/2510.06078/) (2025)

## Abstract (arXiv)

Deep Reinforcement Learning (DRL) has shown a dramatic improvement in decision-making and automated control problems. Consequently, DRL represents a promising technique to efficiently solve many relevant optimization problems (e.g., routing) in self-driving networks. However, existing DRL-based solutions applied to networking fail to generalize, which means that they are not able to operate properly when applied to network topologies not observed during training. This lack of generalization capability significantly hinders the deployment of DRL technologies in production networks. This is because state-of-the-art DRL-based networking solutions use standard neural networks (e.g., fully connected, convolutional), which are not suited to learn from information structured as graphs. In this paper, we integrate Graph Neural Networks (GNN) into DRL agents and we design a problem specific action space to enable generalization. GNNs are Deep Learning models inherently designed to generalize over graphs of different sizes and structures. This allows the proposed GNN-based DRL agent to learn and generalize over arbitrary network topologies. We test our DRL+GNN agent in a routing optimization use case in optical networks and evaluate it on 180 and 232 unseen synthetic and real-world network topologies respectively. The results show that the DRL+GNN agent is able to outperform state-of-the-art solutions in topologies never seen during training.