Optimization of Logistics Information System based on Multi-Agent Reinforcement Learning

Real-time traffic management and optimization are crucial for the efficiency and sustainability of logistics information systems. This research work proposes an innovative approach to optimize logistics information systems using a multi-agent reinforcement learning algorithm. The research focuses on improving the stability level of the information system and introduces a novel optimization method based on multi-agent reinforcement learning. The algorithm is applied to improve the efficiency of data processing in the system. The study also includes the design of an intelligent logistics information system with emphasis on data collection, transmission, processing, storage, and output. The proposed system utilizes embedded system design principles and is implemented on the Android platform. The core of the multi-agent reinforcement learning algorithm is the Q-learning model, which enables the system to acquire knowledge and make optimal decisions. The designed logistics information system undergoes rigorous testing to ensure stability, reliability, and security under various conditions. Experimental results show that the system meets the specified design requirements and provides users with a fast and stable experience. The integration of the system clock improves the accuracy of data analysis. This study contributes to the ongoing development of intelligent logistics information systems, and addresses the need for efficient, stable, and secure solutions in modern logistics management.