  Reinforcement Learning (RL) presents a new approach for controlling Adaptive
Optics (AO) systems for Astronomy. It promises to effectively cope with some
aspects often hampering AO performance such as temporal delay or calibration
errors. We formulate the AO control loop as a model-based RL problem (MBRL) and
apply it in numerical simulations to a simple Shack-Hartmann Sensor (SHS) based
AO system with 24 resolution elements across the aperture. The simulations show
that MBRL controlled AO predicts the temporal evolution of turbulence and
adjusts to mis-registration between deformable mirror and SHS which is a
typical calibration issue in AO. The method learns continuously on timescales
of some seconds and is therefore capable of automatically adjusting to changing
conditions.
