Current and future high-contrast imaging instruments require extreme adaptive optics
systems to reach contrasts necessary to directly imaged exoplanets. Telescope vibrations
and the temporal error induced by the latency of the control loop limit the performance of
these systems. One way to reduce these effects is to use predictive control. We describe how
model-free reinforcement learning can be used to optimize a recurrent neural network
controller for closed-loop predictive control. First, we verify our proposed approach for tip–tilt …

Current and future high-contrast imaging instruments require extreme Adaptive Optics (XAO)
systems to reach contrasts necessary to directly image exoplanets. Telescope vibrations and
the temporal error induced by the latency of the control loop limit the performance of these
systems. Optimization of the (predictive) control algorithm is crucial in reducing these effects.
We describe how model-free Reinforcement Learning can be used to optimize a Recurrent
Neural Network controller for closed-loop adaptive optics control. We verify our proposed …