Research on library resource optimization algorithm based on reinforcement learning

This paper aims to explore the application and optimization effect of context-aware deep reinforcement learning (CDR-L) and resource management system (RMS) integrated model in library resource management. The CDR-L RMS model innovatively integrates collaborative filtering mechanism and deep duplicative strategy gradient (DDPG) algorithm, aiming to realize accurate recommendation of library books and efficient dynamic adjustment of inventory management, so as to meet the diversified and personalized needs of users, optimize resource allocation and reduce operating costs. Firstly, this paper analyzes the challenges existing in current library resource management. Then, the design principle and architecture of the CDR-L RMS model are introduced in detail, which dynamically adjusts the recommendation strategy and inventory optimization decision, in order to achieve the goal of improving user satisfaction, increasing the circulation rate of books and improving the efficiency of resource use. The experimental results show that compared with traditional resource management methods, CDR-L RMS model can significantly increase the amount of books borrowed.