Abstract: The problem of path search in a complex network is a difficult point in network optimization. Existing path search algorithms mainly have the following problems: Firstly, they can only focus on one of solution efficiency and solution accuracy; secondly, they are not adaptable to complex networks with dynamic changes, and the solution effect is not good. So this paper proposes an improved path search algorithm based on double layering and optimized Q-Learning. For the problem that the solution time increases sharply with the increase of scale, a dual-layered strategy of dividing the network by combining k-core and modularity is proposed to reduce the network size reasonably and effectively. In the subnet solution, the reinforcement learning mechanism is introduced to dynamically sense the network. For the problem of slow convergence of the algorithm, the adaptive learning factor and memory factor are added to optimize the update formula and improve the convergence speed. Finally, under different power-law exponents (2 to 3) and complex networks of different sizes, the proposed algorithm is compared with Dijkstra algorithm, A* algorithm and Qrouting algorithm. The results show that the algorithm can effectively improve the solution efficiency while ensuring a good solution accuracy.

Key words: complex network, path optimization, hierarchical network, reinforcement learning