Topology-Aware Deep Reinforcement Learning for Dynamic Multicast Routing in Software-Defined Networks

Dynamic multicast routing in software-defined networks is challenging due to continuously changing network states, multicast branch coupling, and the dependency between local forwarding decisions and global multicast tree construction. Existing multicast routing approaches mainly rely on static heuristics or snapshot-based optimization, which makes them difficult to maintain routing adaptability and decision stability under dynamic network conditions. To address these limitations, this paper proposes a topology-aware deep reinforcement learning multicast routing algorithm, named Graph-structured Hierarchical Actor–Critic for Multicast Routing (GHAC-MR). Specifically, the multicast routing process is formulated as a sequential tree construction problem, where each forwarding action incrementally affects the subsequent multicast tree evolution. A graph-structured state representation mechanism is designed to encode network topology information, link resource states, and multicast branch dependencies, enabling the routing agent to capture structural correlations among multicast forwarding nodes. Furthermore, a hierarchical actor–critic learning architecture is introduced to jointly optimize multicast forwarding policies and long-term routing rewards, thereby improving routing adaptability and convergence stability in dynamic network environments. Experimental results on multiple representative network topologies demonstrate that the proposed GHAC-MR algorithm achieves superior performance in multicast acceptance ratio, resource utilization efficiency, and routing adaptability compared with representative heuristic, evolutionary, and reinforcement learning-based multicast routing schemes.