Enabling Robust and Generalized DRL-Driven Routing Optimization via Exploration of Graph Neural Networks

Packet routing in modern communication networks faces increasing challenges due to evolving network dynamics and stringent quality-of-service (QoS) requirements. Traditional rule-based and model-based routing methods, constrained by pre-defined rules and static network assumptions, often fail to address these complexities. This paper presents an experience-driven, model-free routing algorithm grounded in deep reinforcement learning (DRL), offering adaptive and efficient forwarding decisions. For practical deployment, we incorporate GraphTransformer networks into the DRL decision framework to enhance generalization under diverse and unseen network conditions. Specifically, we explore two element-level mapping paradigms, node-based and edge-based, to identify the optimal configuration. In addition, we design a hybrid action space that effectively incorporates domain-specific knowledge, thereby contributing to a more robust DRL agent in dynamic routing environments. Simulation results indicate that our proposed algorithms demonstrate significant robustness and generalization capabilities, revealing key principles for designing an optimal GNN-enhanced DRL-based routing algorithm.