The Effect of the Number of Hidden Layers on The Performance of Deep Q-Network for Traveling Salesman Problem

Abstract

The Traveling Salesman Problem (TSP) effectively represents the complex distribution issues encountered by couriers, who must carefully plan a route that includes all customer addresses while minimizing the distance traveled. As the magnitude of deliveries and the range of destinations expand, the courier's responsibility becomes progressively challenging. In this particular context, the objective of our research is to expand the existing knowledge and explore the complete capabilities of Deep Q-Network (DQN) models in order to achieve the most efficient route determination. This endeavor can potentially bring about significant changes in the courier and delivery service sector. The foundation of our unique methodology relies on an empirical inquiry, utilizing a comprehensive dataset including 178 observations obtained from motorcycle-based package delivery agents. Our research is carefully planned and executed using a comprehensive factorial experimental design. This design incorporates three crucial factors: the number of hidden layers, episodes, and epochs. The hidden layer parameter is set to a singular level, while the episode parameter is configured to explore five levels, and the epoch parameter is designed to travel four levels. The evaluation of our DQN models' performance is conducted utilizing the MSE metric as a measure. This assessment is carried out at every iterative cycle, ensuring thorough scrutiny. The central focus of our research centers on the intricate connection between episodes and epochs, and their influence on MSE. The findings of our study reveal that the association between episodes, epochs, and errors is not statistically significant although different level of episodes and epochs produces slightly different level of error.