重新思考基于轻量解码器的车辆路径问题求解器
Rethinking Light Decoder-based Solvers for Vehicle Routing Problems
AI总结：
本文分析了轻量解码器在车辆路径问题中因静态嵌入导致高信息密度且解码能力不足而泛化受限的问题，通过简单添加恒等映射和前馈层增强解码器容量，显著提升了大规模实例和复杂变体的分布外泛化能力。
摘要：
基于轻量解码器的求解器因其高效性以及与强化学习算法易于集成的特点，在求解车辆路径问题（VRPs）方面变得流行。然而，它们在泛化到更大规模的问题实例或不同的VRP变体时常常面临困难。本文重新审视了基于轻量解码器的方法，分析了其依赖静态嵌入所带来的影响以及由此产生的固有挑战。具体而言，我们证明了在轻量解码器范式中，编码器被隐式地要求在单一嵌入集合内，捕捉解构建过程中所有潜在决策场景的信息，这导致了高信息密度。此外，我们的实证分析揭示，过于简单的解码器难以有效利用这些密集的信息，尤其是随着任务复杂度的增加，这限制了其在分布外（OOD）设置下的泛化能力。基于这些见解，我们表明通过简单添加恒等映射和前馈层来增强解码器容量，可以显著缓解泛化问题。实验表明，我们的方法显著提升了基于轻量解码器的方法在大规模实例和复杂VRP变体上的OOD泛化能力，缩小了与重量级解码器范式之间的差距。我们的代码可在以下网址获取：https://github.com/ziweileonhuang/reld-nco。
英文摘要：
Light decoder-based solvers have gained popularity for solving vehicle routing problems (VRPs) due to their efficiency and ease of integration with reinforcement learning algorithms. However, they often struggle with generalization to larger problem instances or different VRP variants. This paper revisits light decoder-based approaches, analyzing the implications of their reliance on static embeddings and the inherent challenges that arise. Specifically, we demonstrate that in the light decoder paradigm, the encoder is implicitly tasked with capturing information for all potential decision scenarios during solution construction within a single set of embeddings, resulting in high information density. Furthermore, our empirical analysis reveals that the overly simplistic decoder struggles to effectively utilize this dense information, particularly as task complexity increases, which limits generalization to out-of-distribution (OOD) settings. Building on these insights, we show that enhancing the decoder capacity, with a simple addition of identity mapping and a feed-forward layer, can considerably alleviate the generalization issue. Experimentally, our method significantly enhances the OOD generalization of light decoder-based approaches on large-scale instances and complex VRP variants, narrowing the gap with the heavy decoder paradigm. Our code is available at: https://github.com/ziweileonhuang/reld-nco.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
