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[2602.13211] An Overlay Multicast Routing Method Based on Network Situational Aware-ness and Hierarchical Multi-Agent Reinforcement Learning
Summary
This article presents MA-DHRL-OM, a novel overlay multicast routing method utilizing multi-agent deep hierarchical reinforcement learning to enhance traffic awareness and optimize network resource utilization.
Why It Matters
As networks become increasingly complex and dynamic, traditional multicast routing methods struggle to adapt. This research introduces an innovative approach that improves routing efficiency, reduces latency, and enhances scalability, making it highly relevant for network engineers and AI researchers focused on network optimization.
Key Takeaways
- MA-DHRL-OM improves overlay multicast routing by leveraging multi-agent reinforcement learning.
- The method enhances adaptability and scalability in dynamic network environments.
- Experimental results demonstrate superior performance in delay, bandwidth utilization, and packet loss compared to existing methods.
Computer Science > Networking and Internet Architecture arXiv:2602.13211 (cs) [Submitted on 17 Jan 2026] 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 View a PDF of the paper titled An Overlay Multicast Routing Method Based on Network Situational Aware-ness and Hierarchical Multi-Agent Reinforcement Learning, by Miao Ye and 6 other authors View PDF 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 ...
