Comparative Analysis of Reinforcement Learning Agents for Optimizing Airfoil Shapes

This work investigates the optimization of airfoil shapes using various reinforcement learning (RL) algorithms, including Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Trust Region Policy Optimization (TRPO). The primary objective is to enhance the aerodynamic performance of airfoils by maximizing lift forces across different angles of attack (AoA). The study compares the optimized airfoils against the standard NACA 2412 airfoil. The DDPG-optimized airfoil demonstrated superior performance at lower and moderate AoAs, while the TRPO-optimized airfoil excelled at higher AoAs. In contrast, the TD3-optimized airfoil consistently underperformed. The results indicate that RL algorithms, particularly DDPG and TRPO, can effectively improve airfoil designs, offering substantial benefits in lift generation. This paper underscores the potential of RL techniques in aerodynamic shape optimization, presenting significant implications for aerospace and related industries.