Barrett discusses the complexities of using neural networks for vehicle routing problems, highlighting the combinatorial nature of the action space. He explains the challenges of training these models due to their size and suggests that while linear approximations are simpler, they may not capture the nuances needed for accurate future value predictions. The conversation reveals the delicate balance between leveraging powerful technologies and addressing significant computational hurdles.
