A Routing Algorithm for Computing Power Network Based on Deep Reinforcement Learning and Graph Neural Networks

The computing power network(CPN), as a current research hotspot, aims to provide users with reliable, efficient, and secure computing capabilities. However, research on the routing algorithm for computing power network is limited. Additionally, existing computing power networks are mainly applied in general domains, lacking improvement methods tailored for special scenarios such as emergency communications, which are prone to vulnerabilities and fluctuations. In this paper, we propose a routing algorithm for computing power network, namely Graph Attention Q-Network(GAQN), which integrates GAT and DQN to optimize routing strategies with the objective of maximizing overall system throughput. To evaluate the performance of GAQN, we conduct comparative experiments with multiple baseline models across various network topologies. Additionally, we perform experiments on topologies with different numbers of node failures. The results demonstrate that the proposed algorithm outperforms the baseline algorithms overall and performs well in disrupted network topologies, proving its generalization and robustness.