In order to extend GAN to multi-tasking mode and construct a high-performance model, this paper combines Reinforcement Learning (RL) agents with GAN to construct a multi-tasking image generation model, RL-GAN. The model performance is improved by replacing the RL agent training algorithm, setting a more reasonable AC network loss function, and replacing the network structure. The experimental results show that:1) The generated results of the model in two multi-tasking image restoration experiments meet visual requirements; 2) Compared with multi-GAN stacking, a mainstream method in current multi-tasking modes, the RL-GAN model has faster convergence and image processing speed, higher output quality, and the accuracy and efficiency of the model are also better after introducing RL agents; and 3) The optimized model significantly improves its multi-tasking processing ability.