Strategy for maximizing space utilization in smart libraries based on reinforcement learning

Smart and modern libraries need robust and sophisticated systems to maximize space use. Traditional library designs are built on unchanging spatial arrangements, making it difficult to manage dynamic user demands due to overcrowded study zones, poor navigation, and wasteful storage. Real-time user behavior fluctuations are ignored in the current design, resulting in space underutilization and overcrowding in high-traffic areas. Introduce Reinforcement Learning Maximize Space Utilization (RLMSU) methodologies to manage dynamic space in modern libraries to solve research problems. The RLMSU platform collects data from IoT sensors, historical usage patterns, and computer vision to improve book-shelf design, seating, and navigation pathways. The agent-action (AA) paradigm predicts user occupancy and movement in the RL method, optimizing space allocation and resource use while maintaining accessibility. Every agent's actions are rewarded under the AA principle, gathering library environment feedback. Python is used to analyze space use and develop the RLMSU framework using the Full Library Services Dataset. Effective use of the AA idea increases seating availability by 30% and reduces congestion by 25%. The system works well in dynamic scenarios and improves user satisfaction during peak and off-peak hours. Hence, this process was successfully integrated with research institutions, university libraries, and public libraries to improve space and operational efficiency.