mlsanityData to 14 Oct 2026

Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach

arXiv 2409.15226 ↗First posted 23 Sep 2024cs.NI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 2 of its 45 references that are in the map. First posted on arXiv 23 Sep 2024.

Builds on

2 of 45 references.

References · 2

  1. 2019Hierarchical Deep Double Q-Routingcited by 6
  2. 2021ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement Learningcited by 4

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024A Deep Reinforcement Learning Approach for Adaptive Traffic Routing in Next-gen Networks
  2. 2025Spatial-Temporal Reinforcement Learning for Network Routing with Non-Markovian Traffic
  3. 2025A New Segment Routing method with Swap Node Selection Strategy Based on Deep Reinforcement Learning for Software Defined Network
  4. 2024Analysis of flexible traffic control method in SDN
  5. 2025Demonstration of effective UCB-based routing in skill-based queues on real-world data
  6. 2024On Joint Convergence of Traffic State and Weight Vector in Learning-Based Dynamic Routing with Value Function Approximation
  7. 2025Simulation-Driven Reinforcement Learning in Queuing Network Routing Optimization
  8. 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics