Distributed Multi-Agent Reinforcement Learning on a Hierarchical Game Model for Railway Engineering Data Collaborative Edge Caching

The rapid expansion and intelligent development of railway infrastructure are driving significant growth in railway engineering data. For the dispersed users across railway networks’ complex topology, traditional centralized storage systems are insufficient for their low-latency, cost-efficient data retrieval. Existing edge caching solutions based on multi-agent reinforcement learning fail to address the asymmetric relationships among railway nodes, such as data centers, stations, and sections, etc. Besides, the complexity of computing Nash equilibrium points also gets higher as the number of agents (edge caching servers) increases. This study introduces a Hierarchical Game model-based MADRL-driven Collaborative Edge Caching method(HG-MCEC) tailored for railway engineering data. By considering the distribution characteristics and caching strategy games among railway nodes, a hierarchical game model for collaborative edge caching is constructed. This model treats the railway edge caching as a multi-agent system, in which each railway node server is regarded as an agent. HG-MCEC utilizes deep learning to mitigate computational complexity and recognize agents’ asymmetry. Upper-level agents adjust cache replacement strategies according to environmental changes and decisionmaking experience. Lower-level agents, under the guidance of upper-level decisions, optimize collaborative caching strategies toward achieving hierarchical game equilibrium. Using a highspeed railway building information modeling data for validation, the method significantly outperforms existing approaches by enhancing content hit rates and reducing latency at edge caching servers while decreasing system content transmission costs.