  This study explores the application of deep reinforcement learning (RL) to
design an airfoil pitch controller capable of minimizing lift variations in
randomly disturbed flows. The controller, treated as an agent in a partially
observable Markov decision process, receives non-Markovian observations from
the environment, simulating practical constraints where flow information is
limited to force and pressure sensors. Deep RL, particularly the TD3 algorithm,
is used to approximate an optimal control policy under such conditions. Testing
is conducted for a flat plate airfoil in two environments: a classical unsteady
environment with vertical acceleration disturbances (i.e., a Wagner setup) and
a viscous flow model with pulsed point force disturbances. In both cases,
augmenting observations of the lift, pitch angle, and angular velocity with
extra wake information (e.g., from pressure sensors) and retaining memory of
past observations enhances RL control performance. Results demonstrate the
capability of RL control to match or exceed standard linear controllers in
minimizing lift variations. Special attention is given to the choice of
training data and the generalization to unseen disturbances.
