  The feasibility of using reinforcement learning for airfoil shape
optimization is explored. Deep Q-Network (DQN) is used over Markov's decision
process to find the optimal shape by learning the best changes to the initial
shape for achieving the required goal. The airfoil profile is generated using
Bezier control points to reduce the number of control variables. The changes in
the position of control points are restricted to the direction normal to the
chordline so as to reduce the complexity of optimization. The process is
designed as a search for an episode of change done to each control point of a
profile. The DQN essentially learns the episode of best changes by updating the
temporal difference of the Bellman Optimality Equation. The drag and lift
coefficients are calculated from the distribution of pressure coefficient along
the profile computed using XFoil potential flow solver. These coefficients are
used to give a reward to every change during the learning process where the
ultimate aim stands to maximize the cumulate reward of an episode.
