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Path planning methods for the unmanned aerial vehicle (UAV) in goods deliveryhave drawn great attention from industry and academics because of itsflexibility which is suitable for many situations in the "Last Kilometer"between customer and delivery nodes. However, the complicated situation isstill a problem for traditional combinatorial optimization methods. Based onthe state-of-the-art Reinforcement Learning (RL), this paper proposed animproved method to achieve path planning for UAVs in complex surroundings:multiple no-fly zones. The improved approach leverages the attention mechanismand includes the embedding mechanism as the encoder and three different widthsof beam search (i.e.,~1, 5, and 10) as the decoders. Policy gradients areutilized to train the RL model for obtaining the optimal strategies duringinference. The results show the feasibility and efficiency of the modelapplying in this kind of complicated situation. Comparing the model with theresults obtained by the optimization solver OR-tools, it improves thereliability of the distribution system and has a guiding significance for thebroad application of UAVs.

Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle