To address the issues of insufficient collaboration among computing resources and poor adaptability to task requirements in computing power networks, the computing power routing problem was modeled as a sequential decision problem. A deep reinforcement learning-based computing-aware routing algorithm was proposed for dynamic routing scheduling of computing network collaboration. The idea of hybrid expert models was drawn on and a differentiated expert network was designed based on an encoder-decoder structure for specialized optimization in three typical scenarios: delay-sensitive, ordinary, and computationally intensive. The routing selection space was constrained through an action masking mechanism to achieve efficient hop-by-hop decision-making and output a path containing the optimal computing node. The simulation experiment results show that compared with other routing scheduling algorithms, the proposed algorithm improves service success rate by about 17%, reduces end-to-end latency, optimizes load balancing between nodes, demonstrates good network topology adaptability, and can effectively meet the differentiated needs of diverse computing tasks
