Airfoil Shape Optimization using Deep Q-Network

@article{Rout2019AirfoilSO,
  title={Airfoil Shape Optimization using Deep Q-Network},
  author={Siddharth Rout and Chao Lin},
  journal={ArXiv},
  year={2019},
  volume={abs/2211.17189}
}
The document explores the feasibility of using reinforcement learning for drag minimization and lift maximization of standard two-dimensional airfoils. Deep Q-network (DQN) is used over Markov’s decision process (MDP) to learn the optimal shape by learning the best changes to the initial shape. The airfoil profile is generated by using Bezier control points. The drag and lift values are calculated from coefficient of pressure values along the profile generated using Xfoil potential flow solver… 
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