Reinforcement Learning Transformer for Image Captioning Generation Model

Image captioning generation is a combination of the visual domain and natural language processing. The transformer framework has become the mainstream approach. This paper combines reinforcement learning and transformer methods to reward dynamics backpropagation and normalization in the testing phase. Its characteristic is that when the steps of reinforcement learning increase, the agent model has more knowledge of the fully information, which reduces the computing cost of the system. The experimental results show that the reinforcement transformer structure has achieved a certain improvement in speed.