An Overlay Multicast Routing Method Based on Network Situational Aware-ness and Hierarchical Multi-Agent Reinforcement Learning

• URL: http://arxiv.org/abs/2602.13211v1
• Date: Sat, 17 Jan 2026 07:50:10 GMT
• Title: An Overlay Multicast Routing Method Based on Network Situational Aware-ness and Hierarchical Multi-Agent Reinforcement Learning
• Authors: Miao Ye, Yanye Chen, Yong Wang, Cheng Zhu, Qiuxiang Jiang, Gai Huang, Feng Ding,
• Abstract summary: We propose MA-DHRL-OM, a multi-agent deep hierarchical reinforcement learning approach.<n>Using SDN's global view, it builds a traffic-aware model for OM path planning.<n>Experiments show MA-DHRL-OM outperforms existing methods in delay, bandwidth utilization, and packet loss.
• Score: 5.087756824536562
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compared with IP multicast, Overlay Multicast (OM) offers better compatibility and flexible deployment in heterogeneous, cross-domain networks. However, traditional OM struggles to adapt to dynamic traffic due to unawareness of physical resource states, and existing reinforcement learning methods fail to decouple OM's tightly coupled multi-objective nature, leading to high complexity, slow convergence, and instability. To address this, we propose MA-DHRL-OM, a multi-agent deep hierarchical reinforcement learning approach. Using SDN's global view, it builds a traffic-aware model for OM path planning. The method decomposes OM tree construction into two stages via hierarchical agents, reducing action space and improving convergence stability. Multi-agent collaboration balances multi-objective optimization while enhancing scalability and adaptability. Experiments show MA-DHRL-OM outperforms existing methods in delay, bandwidth utilization, and packet loss, with more stable convergence and flexible routing.

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