DHRL-FNMR: An Intelligent Multicast Routing Approach Based on Deep Hierarchical Reinforcement Learning in SDN

1 code implementation • 30 May 2023 • Miao Ye, Chenwei Zhao, Xingsi Xue, Jinqiang Li, Hongwen Hu, Yejin Yang, Qiuxiang Jiang

Although existing SDN intelligent solution methods, which are based on deep reinforcement learning, can dynamically adapt to complex network link state changes, these methods are plagued by problems such as redundant branches, large action space, and slow agent convergence.

Combinatorial Optimization Hierarchical Reinforcement Learning +1

A Novel Self-Supervised Learning-Based Anomaly Node Detection Method Based on an Autoencoder in Wireless Sensor Networks

1 code implementation • 26 Dec 2022 • Miao Ye, Qinghao Zhang, Xingsi Xue, Yong Wang, Qiuxiang Jiang, Hongbing Qiu

Due to the issue that existing wireless sensor network (WSN)-based anomaly detection methods only consider and analyze temporal features, in this paper, a self-supervised learning-based anomaly node detection method based on an autoencoder is designed.

Anomaly Detection Self-Supervised Learning

DRL-M4MR: An Intelligent Multicast Routing Approach Based on DQN Deep Reinforcement Learning in SDN

1 code implementation • 31 Jul 2022 • Chenwei Zhao, Miao Ye, Xingsi Xue, Jianhui Lv, Qiuxiang Jiang, Yong Wang

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding.