Abstract: To address issues such as poor convergence performance and weak generalization capability in traditional deep reinforcement learning (DRL) applications for routing optimization in software defined optical networks (SDONs),this paper proposes a multi-view graph attention mechanism-based deep Q-Network (MGATDQN) algorithm to optimize routing decisions in SDONs.First,a DRL-based routing decision model is designed to identify the optimal routing strategy for each source-destination traffic demand in the optical network.Second,considering the sparse connectivity characteristics of nodes in optical networks,a multi-view attention network is employed as the network model for the deep Q-Network (DQN).By computing attention weights for neighboring edges,the reinforcement learning agent can consciously aggregate critical network information,thereby enhancing the model’s generalization capability.Additionally,the integration of multi-view learning improves the convergence speed and stability of the graph attention network model.Finally,simulation-based routing experiments are conducted using the Gym framework,and the algorithm’s load-balancing capability and generalization performance are evaluated across different network topologies.Experimental results demonstrate that the MGATDQN algorithm exhibits superior convergence performance and load-balancing ability in SDON routing optimization.Moreover,it generalizes well to unseen network structures and maintains robust decision-making capabilities even when certain network nodes fail.

Key words: optical transport network, software-defined networking, deep reinforcement learning, multi-view graph attention mechanism