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Traditional multicast routing methods have some problems in constructing amulticast tree, such as limited access to network state information, pooradaptability to dynamic and complex changes in the network, and inflexible dataforwarding. To address these defects, the optimal multicast routing problem insoftware-defined networking (SDN) is tailored as a multi-objective optimizationproblem, and an intelligent multicast routing algorithm DRL-M4MR based on thedeep Q network (DQN) deep reinforcement learning (DRL) method is designed toconstruct a multicast tree in SDN. First, the multicast tree state matrix, linkbandwidth matrix, link delay matrix, and link packet loss rate matrix aredesigned as the state space of the DRL agent by combining the global view andcontrol of the SDN. Second, the action space of the agent is all the links inthe network, and the action selection strategy is designed to add the links tothe current multicast tree under four cases. Third, single-step and finalreward function forms are designed to guide the intelligence to make decisionsto construct the optimal multicast tree. The experimental results show that,compared with existing algorithms, the multicast tree construct by DRL-M4MR canobtain better bandwidth, delay, and packet loss rate performance aftertraining, and it can make more intelligent multicast routing decisions in adynamic network environment.

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