Abstract: Reinforcement learning as a machine learning method for learning policies learns in a way similar to human learning process, interacting with the environment and learning how to achieve more rewards. The elements and algorithms of reinforcement learning are defined and adjusted in this paper for the supercritical airfoil aerodynamic design process. The results of imitation learning are then studied, and the policies from the imitation learning are adopted in reinforcement learning. The influence of different pretraining processes is studied, and the final policies tested in other similar environments. The results show that pretraining can improve reinforcement learning efficiency and policy robustness. The final policies obtained in this study can also have satisfactory performance in other similar environments.

Key words: reinforcement learning, incremental modification, Proximal Policy Optimization (PPO), pretraining, imitation learning, application transferability