Path planning for unmanned vehicles based on reinforcement learning

In recent years, the rapid development of the express delivery industry has made the logistics delivery problem a key bottleneck in the logistics field.This problem not only affects delivery efficiency, but also increases operating costs and reduces customer satisfaction.With the development of science and technology, the application of unmanned vehicles has provided a new opportunity to solve this problem.This paper aims to optimize the logistics delivery path by improving the Q-learning algorithm in reinforcement learning to improve logistics efficiency and customer experience.In the study, we first established a simulated urban delivery environment and defined the state, action and reward functions.Using the improved Q-learning algorithm, the delivery path was trained and optimized multiple times to find the optimal path strategy.The experimental results show that compared with the traditional algorithm, the Q-learning algorithm performs well in path optimization and time saving, and effectively improves the delivery efficiency.Through this study, the potential of reinforcement learning in logistics distribution optimization is verified, and it provides a valuable reference for future intelligent logistics systems.