In recent years, great progress has been made in controlling the categories or attributes of generated images by adding condition tags to the generation adversarial networks. However, the accuracy of the category or attribute of generated image needs to be improved. In order to solve this problem,we add reinforcement learning to the generator of generation adversarial networks, which guides the current classification by previous. In addition, the attention mechanism is used which makes a global sensory field to the images with only a small amount of computational loss. We combines multi-attribute star generation adversarial networks with self-attention generation adversarial networks which improves the quality of generated. maximum mean discrepancy reaches to 0.036 93 and the 1-nearest neighbor classifier has a batter effect by reinforced-combined generative adversarial networks, which can generate the art images that certain attributes are assigned automatically and accurately. The generated images can also be used to address the lack of data.