CGTR: Leveraging Contrastive Learning and Graph Transformer for Deep Reinforcement Learning Based Robust Routing

As a crucial role in communication networks, the routing algorithm determines how to transmit traffic from sources to destinations. In recent years, Deep Reinforcement Learning (D RL) has been introduced into routing algorithms to address dynamic traffic demands in complex networks. However, most DRL-based routing algorithms are implemented by traditional neural networks, which can only handle fixed-size matrices and operate on a fixed topology. Fortunately, Graph Neural Network (GNN) has been proposed to process graph-structured data and generalize on different graphs. Furthermore, contrastive learning has also been successfully applied to DRL for decision-making in multiple environments. In this paper, we introduce GNN and contrastive learning to DRL-based routing algorithms, and propose a Contrastive Graph Transformer Routing (CGTR) algorithm to improve the robustness of routing against link fail-ures. Aiming at probabilistic packet routing scenarios, we design Edge-enhanced Graph Transformer and Contrastive Grouping Routing in CG TR, enabling it to perceive unseen link failures without retraining. To evaluate the robustness of CG TR, we conduct extensive experiments with different patterns of traffic demands on both generated and real-world network topologies. The experimental results demonstrate that CGTR outperforms all baselines on unseen topologies with link failures, highlighting its stronger robustness.