Research on terminal logistics distribution based on reinforcement learning and pointer network

The path planning problem at the end of the logistics is an important task for logistics companies today. Due to the continuous expansion of the logistics scale and the requirements of customer delivery, there is a higher requirement for the path planning algorithm. This paper proposes a distribution path planning algorithm based on pointer network. For the training based on supervised learning, the final results will depend on the quality of the solution in the training data, and the optimal solution is difficult to obtain. In this paper, we propose a reinforcement learning method to train the model, which solves the problem of high cost of training samples and improves model accuracy. Because the input of the terminal logistics distribution problem has the characteristics of sequence independence, this paper studies the multi-head attention mechanism. By capturing the multiple features in the input sequence information, the speed of network convergence is accelerated. The experimental results and analysis indicate that the model proposed in this paper has great improvement in solving speed and good performance in effect.