Reinforcement learning has played an important role in decision making related applications, e.g., robotics motion, self-driving, recommendation, etc. The reward function, as a crucial component, affects the efficiency and effectiveness of reinforcement learning to a large extent. In this paper, we focus on the investigation of reinforcement learning with more than one auxiliary reward. It is found that different auxiliary rewards can boost up the learning rate and effectiveness in different stages, and consequently we propose the Automatic Successive Reinforcement Learning (ASR) for auxiliary rewards grading selection for efficient reinforcement learning by stages. Experiments and simulations have shown the superiority of our proposed ASR on a range of environments, including OpenAI classical control domains and video games; Freeway and Catcher.