ERA-MADDPG: An Elastic Routing Algorithm Based on Multi-Agent Deep Deterministic Policy Gradient in SDN

Wanwei Huang, Hongchang Liu, Yingying Li () and Linlin Ma
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Wanwei Huang: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450007, China
Hongchang Liu: College of Software Engineering, Zhengzhou University of Light Industry, Zhengzhou 450007, China
Yingying Li: College of Electronics & Communication Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China
Linlin Ma: College of Information Technology, Zhengzhou Vocational College of Finance and Taxation, Zhengzhou 450048, China

Future Internet, 2025, vol. 17, issue 7, 1-20

Abstract: To address the fact that changes in network topology can have an impact on the performance of routing, this paper proposes an Elastic Routing Algorithm based on Multi-Agent Deep Deterministic Policy Gradient (ERA-MADDPG), which is implemented within the framework of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) in deep reinforcement learning. The algorithm first builds a three-layer architecture based on Software-Defined Networking (SDN). The top-down layers are the multi-agent layer, the controller layer, and the data layer. The architecture’s processing flow, including real-time data layer information collection and dynamic policy generation, enables the ERA-MADDPG algorithm to exhibit strong elasticity by quickly adjusting routing decisions in response to topology changes. The actor-critic framework combined with Convolutional Neural Networks (CNN) to implement the ERA-MADDPG routing algorithm effectively improves training efficiency, enhances learning stability, facilitates collaboration, and improves algorithm generalization and applicability. Finally, simulation experiments demonstrate that the convergence speed of the ERA-MADDPG routing algorithm outperforms that of the Multi-Agent Deep Q-Network (MADQN) algorithm and the Smart Routing based on Deep Reinforcement Learning (SR-DRL) algorithm, and the training speed in the initial phase is improved by approximately 20.9% and 39.1% compared to the MADQN algorithm and SR-DRL algorithm, respectively. The elasticity performance of ERA-MADDPG is quantified by re-convergence speed: under 5–15% topology node/link changes, its re-convergence speed is over 25% faster than that of MADQN and SR-DRL, demonstrating superior capability to maintain routing efficiency in dynamic environments.

Keywords: DDPG; multi-agent; network topology; routing algorithm; SDN; actor-critic; DRL (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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