论文标题
多动物的注意模型，嵌入瞥见以解决车辆路线问题
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems
论文作者
论文摘要
我们提出了一种新颖的深入增强学习方法，以学习用于车辆路线问题的施工启发式方法。具体而言，我们提出了一个多二十座关注模型（MDAM）来培训多种不同的政策，这实际上增加了与仅培训一项政策的现有方法相比，找到良好解决方案的机会。定制的光束搜索策略旨在充分利用MDAM的多样性。此外，我们根据建筑的递归性质提出了MDAM中嵌入的瞥见层，该层次可以通过提供更多信息丰富的嵌入来提高每个政策的质量。关于六个不同路由问题的广泛实验表明，我们的方法显着胜过最先进的基于深度学习的模型。
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) to train multiple diverse policies, which effectively increases the chance of finding good solutions compared with existing methods that train only one policy. A customized beam search strategy is designed to fully exploit the diversity of MDAM. In addition, we propose an Embedding Glimpse layer in MDAM based on the recursive nature of construction, which can improve the quality of each policy by providing more informative embeddings. Extensive experiments on six different routing problems show that our method significantly outperforms the state-of-the-art deep learning based models.
