A Deep-Reinforcement Learning Approach for Software-Defined Networking Routing Optimization
Not on the map. Why?
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.
Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Sep 2017.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 6; 2022: 1; 2023: 4; 2024: 1; 2025: 1; 2026: 1.
14 papers.
Sort:
- 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
- 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineeringcited by 10
- 2021GDDR: GNN-based Data-Driven Routingcited by 9
- 2021Is Machine Learning Ready for Traffic Engineering Optimization?cited by 8
- 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networkscited by 6
- 2021Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETscited by 5
- 2021A Reinforcement Learning Approach for Scheduling in mmWave Networkscited by 3
- 2023DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learningcited by 2
- 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamicscited by 2
- 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocolscited by 1
- 2025Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networkscited by 1
- 2022A Review of the Convergence of 5G/6G Architecture and Deep Learningcited by 0
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications4 Mar 2026
- 2025Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks20 May 2025
- 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics14 Oct 2024
- 2023DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning16 Jun 2023
- 2023Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks12 May 2023
- 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering31 Mar 2023
- 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols9 Feb 2023
- 2022A Review of the Convergence of 5G/6G Architecture and Deep Learning16 Aug 2022
- 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks29 Nov 2021
- 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey26 Oct 2021
- 2021Is Machine Learning Ready for Traffic Engineering Optimization?3 Sep 2021
- 2021A Reinforcement Learning Approach for Scheduling in mmWave Networks1 Aug 2021
2 more
- 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Surveycited by 34
- 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineeringcited by 10
- 2021GDDR: GNN-based Data-Driven Routingcited by 9
- 2021Is Machine Learning Ready for Traffic Engineering Optimization?cited by 8
- 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networkscited by 6
- 2021Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for MANETscited by 5
- 2021A Reinforcement Learning Approach for Scheduling in mmWave Networkscited by 3
- 2023DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learningcited by 2
- 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamicscited by 2
- 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocolscited by 1
- 2025Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networkscited by 1
- 2022A Review of the Convergence of 5G/6G Architecture and Deep Learningcited by 0
12 most cited of 14 All 14 (JSON)
- 2026A Constrained RL Approach for Cost-Efficient Delivery of Latency-Sensitive Applications4 Mar 2026
- 2025Interpretable Reinforcement Learning for Load Balancing using Kolmogorov-Arnold Networks20 May 2025
- 2024Learning Sub-Second Routing Optimization in Computer Networks requires Packet-Level Dynamics14 Oct 2024
- 2023DeepMPR: Enhancing Opportunistic Routing in Wireless Networks through Multi-Agent Deep Reinforcement Learning16 Jun 2023
- 2023Multi-Agent Reinforcement Learning for Network Routing in Integrated Access Backhaul Networks12 May 2023
- 2023MAGNNETO: A Graph Neural Network-based Multi-Agent system for Traffic Engineering31 Mar 2023
- 2023RayNet: A Simulation Platform for Developing Reinforcement Learning-Driven Network Protocols9 Feb 2023
- 2022A Review of the Convergence of 5G/6G Architecture and Deep Learning16 Aug 2022
- 2021DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks29 Nov 2021
- 2021Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey26 Oct 2021
- 2021Is Machine Learning Ready for Traffic Engineering Optimization?3 Sep 2021
- 2021A Reinforcement Learning Approach for Scheduling in mmWave Networks1 Aug 2021
12 newest of 14 All 14 (JSON)