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Virtual Network Embedding (VNE) is a crucial problem in network virtualization. Prior work on VNE is mainly focused on optimization-based solutions that are carefully constructed and tuned under specific assumptions about resource demands brought by virtual networks. Recently, a few works have appeared on automating the design of VNE solutions that work well under general virtual resource demands using Deep Reinforcement Learning (DRL). These works, however, still rely on manual selection of relevant problem features required in the DRL approach. In this work, we develop a DRL-based VNE solution called DeepViNE, which automates the selection of problem features required in the DRL approach. The key idea is to encode physical and virtual networks as two-dimensional images, which are then perceivable by a convolutional deep neural network. To speed up learning and algorithm convergence …