# CFR-RL: Traffic Engineering with Reinforcement Learning in SDN

arXiv: https://arxiv.org/abs/2004.11986 · HTML: https://mlsanity.com/paper/2004.11986/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization](https://mlsanity.com/paper/2102.11055/) (2021)
- [Graph Reinforcement Learning for Combinatorial Optimization: A Survey and Unifying Perspective](https://mlsanity.com/paper/2404.06492/) (2024)
- [Teal: Learning-Accelerated Optimization of WAN Traffic Engineering](https://mlsanity.com/paper/2210.13763/) (2022)
- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN](https://mlsanity.com/paper/2208.00383/) (2022)
- [ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learning](https://mlsanity.com/paper/2109.10883/) (2021)
- [Graph Neural Modeling of Network Flows](https://mlsanity.com/paper/2209.05208/) (2022)
- [Action-Constrained Imitation Learning](https://mlsanity.com/paper/2508.14379/) (2025)
- [Intelligent multicast routing method based on multi-agent deep reinforcement learning in SDWN](https://mlsanity.com/paper/2305.10440/) (2023)
- [Topology-Aware Graph Reinforcement Learning for Dynamic Routing in Cloud Networks](https://mlsanity.com/paper/2509.04973/) (2025)
- [Robust Path Selection in Software-defined WANs using Deep Reinforcement Learning](https://mlsanity.com/paper/2212.11155/) (2022)
- [Reinforcement Learning Discovers Efficient Decentralized Graph Path Search Strategies](https://mlsanity.com/paper/2409.07932/) (2024)

## Abstract (arXiv)

Traditional Traffic Engineering (TE) solutions can achieve the optimal or near-optimal performance by rerouting as many flows as possible. However, they do not usually consider the negative impact, such as packet out of order, when frequently rerouting flows in the network. To mitigate the impact of network disturbance, one promising TE solution is forwarding the majority of traffic flows using Equal-Cost Multi-Path (ECMP) and selectively rerouting a few critical flows using Software-Defined Networking (SDN) to balance link utilization of the network. However, critical flow rerouting is not trivial because the solution space for critical flow selection is enormous. Moreover, it is impossible to design a heuristic algorithm for this problem based on fixed and simple rules, since rule-based heuristics are unable to adapt to the changes of the traffic matrix and network dynamics. In this paper, we propose CFR-RL (Critical Flow Rerouting-Reinforcement Learning), a Reinforcement Learning-based scheme that learns a policy to select critical flows for each given traffic matrix automatically. CFR-RL then reroutes these selected critical flows to balance link utilization of the network by formulating and solving a simple Linear Programming (LP) problem. Extensive evaluations show that CFR-RL achieves near-optimal performance by rerouting only 10%-21.3% of total traffic.