Logistics distribution path optimization based on deep reinforcement learning

Aiming at the optimization of logistics distribution routes in the current domestic dynamic traffic environment, this paper proposes a self-coding network intelligent decision-making model based on deep reinforcement learning. During the construction of the model, traffic data is divided into three scenarios: regular working days, meteorological early warning days and holidays, combined with the road-level meteorological response model and the dynamic weighted road network calculation method. The traditional static path planning model effectively solves the defect of insufficient adaptability to dynamic factors such as sudden congestion and weather disturbance. Through algorithm experiments, it is found that the deep reinforcement learning framework can effectively alleviate the path planning problems under high-load traffic operation through spatiotemporal feature mining and dynamic weight optimization, and provide key technical support for the construction of intelligent logistics system, which has the dual value of promoting the intelligent transformation of the industry and urban traffic governance.