针对超临界翼型气动修型策略的强化学习

强化学习是一类用于学习策略的机器学习方法，通过模拟人的学习过程，与所处环境不断交互来学习动作策略，用以获得最大累积回报。以设计师在翼型气动设计中的增量修型过程为例，给出强化学习在气动优化设计中的要素定义和具体算法的实现。研究了预训练中选择不同示例对预训练和强化学习结果的影响，并将强化学习得到的策略模型在其他环境中进行了迁移测试验证。结果表明，合理的预训练能够有效提高强化学习的效率和最终策略的鲁棒性，且所形成的策略模型具有较好的迁移能力。

Reinforcement learning method for supercritical airfoil aerodynamic design

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.