  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.
