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We present a novel deep reinforcement learning method to learn constructionheuristics for vehicle routing problems. In specific, we propose aMulti-Decoder Attention Model (MDAM) to train multiple diverse policies, whicheffectively increases the chance of finding good solutions compared withexisting methods that train only one policy. A customized beam search strategyis designed to fully exploit the diversity of MDAM. In addition, we propose anEmbedding Glimpse layer in MDAM based on the recursive nature of construction,which can improve the quality of each policy by providing more informativeembeddings. Extensive experiments on six different routing problems show thatour method significantly outperforms the state-of-the-art deep learning basedmodels.

Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing Problems