A set of reinforcement learning (RL) methods for predictive control in adaptive optics.
Through trial and error we learn a function that maps information of a wavefront sensor (WFS) to commands to a deformable mirror to correct for wavefront distortions created by the atmosphere when observing with ground-based telescopes. We have tested different wavefront sensors and instrument simulations.
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