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September 10, 2025Open Access

Advances in Q-Learning: Real-Time Optimization of Multi-Distant Transportation Systems

Key Points

Q-learning achieved superior performance in optimizing vehicle routing, generating solutions close to global optima.
In comparative analysis, Q-learning excelled over traditional algorithms, showing better accuracy and speed.
This project utilized modern machine learning techniques to address the traveling salesman and vehicle routing problems.
The findings suggest that Q-learning can effectively handle dynamic environments and large-scale problem instances.

Abstract

Vehicle routing improvement has become a vital topic in modern transport digitalization projects. Presently, there are no fully adapted techniques to offer optimal solutions for finding the best routes that include all visiting locations, considering mandatory transportation constraints. This project explores the application of modern machine learning techniques in solving transportation problems, with a specific focus on Q-learning. We utilize Q-learning to address the traveling salesman and vehicle routing problems. The ability of Q-learning to find optimal solutions in dynamic environments helped overcome the vulnerabilities of traditionally used algorithms. Moreover, this project provides an advanced comparative analysis in terms of accuracy and speed between Q-learning and currently used algorithms in the same scope, using a set of generated routing datasets. Q-learning presented superior performance, generating solutions that were closest to the global optima, exhibiting impressive computational efficiency and fast action even with large-scale problem instances, suggesting that it can serve as a powerful tool for optimizing transportation systems.

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