  Recently it has shown that the policy-gradient methods for reinforcement
learning have been utilized to train deep end-to-end systems on natural
language processing tasks. What's more, with the complexity of understanding
image content and diverse ways of describing image content in natural language,
image captioning has been a challenging problem to deal with. To the best of
our knowledge, most state-of-the-art methods follow a pattern of sequential
model, such as recurrent neural networks (RNN). However, in this paper, we
propose a novel architecture for image captioning with deep reinforcement
learning to optimize image captioning tasks. We utilize two networks called
"policy network" and "value network" to collaboratively generate the captions
of images. The experiments are conducted on Microsoft COCO dataset, and the
experimental results have verified the effectiveness of the proposed method.
