In this paper we present a new approach to tackle complex routing problems with an improved state representation that utilizes the model complexity better than previous methods. We enable this by training from temporal differences. Specifically Q-Learning is employed. We show that our approach achieves state-of-the-art performance for autoregressive policies that sequentially insert nodes to construct solutions on the CVRP. Additionally, we are the first to tackle the MDVRP with machine learning methods and demonstrate that this problem type greatly benefits from our approach over other ML methods.
@article{arxiv.2104.12226,
title = {RP-DQN: An application of Q-Learning to Vehicle Routing Problems},
author = {Ahmad Bdeir and Simon Boeder and Tim Dernedde and Kirill Tkachuk and Jonas K. Falkner and Lars Schmidt-Thieme},
journal= {arXiv preprint arXiv:2104.12226},
year = {2021}
}