# A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization

arXiv: https://arxiv.org/abs/1709.07080 · HTML: https://mlsanity.com/paper/1709.07080/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2017.

## Built on by (most cited first, 14 of 14)

- [Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey](https://mlsanity.com/paper/2110.13484/) (2021)
- [MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering](https://mlsanity.com/paper/2303.18157/) (2023)
- [GDDR: GNN-based Data-Driven Routing](https://mlsanity.com/paper/2104.09919/) (2021)
- [Is Machine Learning Ready for Traffic Engineering Optimization?](https://mlsanity.com/paper/2109.01445/) (2021)
- [DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks](https://mlsanity.com/paper/2111.15013/) (2021)
- [Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETs](https://mlsanity.com/paper/2101.03273/) (2021)
- [A Reinforcement Learning Approach for Scheduling in mmWave Networks](https://mlsanity.com/paper/2108.00548/) (2021)
- [DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2306.09637/) (2023)
- [Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics](https://mlsanity.com/paper/2410.10377/) (2024)
- [RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols](https://mlsanity.com/paper/2302.04519/) (2023)
- [Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2505.14459/) (2025)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks](https://mlsanity.com/paper/2305.16170/) (2023)
- [A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications](https://mlsanity.com/paper/2603.04353/) (2026)

## Abstract (arXiv)

In this paper we design and evaluate a Deep-Reinforcement Learning agent that optimizes routing. Our agent adapts automatically to current traffic conditions and proposes tailored configurations that attempt to minimize the network delay. Experiments show very promising performance. Moreover, this approach provides important operational advantages with respect to traditional optimization algorithms.