用分层强化学习优化跨域组播路由，提升稳定性与适应性
An Overlay Multicast Routing Method Based on Network Situational Awareness and Hierarchical Multi-Agent Reinforcement Learning
- 分两阶段构建组播树，降低动作空间复杂度
- 延迟更低、带宽利用率更高，包丢失率下降显著
- 适合动态网络环境，尤其适用于大规模跨域场景
与IP组播相比，覆盖组播（OM）在异构、跨域网络中具有更好的兼容性和灵活部署能力。然而，传统OM因缺乏对物理资源状态的感知，难以适应动态流量；现有强化学习方法又无法解耦OM紧密耦合的多目标特性，导致复杂度高、收敛慢且不稳定。为此，我们提出MA-DHRL-OM，一种基于软件定义网络（SDN）全局视图的多智能体深度分层强化学习方法。通过分层代理将组播树构建分为两个阶段，减少动作空间，提升收敛稳定性。多智能体协作实现多目标优化，增强可扩展性与适应性。实验表明，该方法在延迟、带宽利用率和包丢失率方面均优于现有方法，收敛更稳定，路由更灵活。
原文摘要 · Abstract (English)
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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