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.
Context. The direct imaging of potentially habitable exoplanets is one prime science case for the next generation of high contrast imaging instruments on ground-based, extremely large telescopes. To reach this demanding science goal, the instruments are equipped with eXtreme Adaptive Optics (XAO) systems which will control thousands of actuators at a framerate of kilohertz to several kilohertz. Most of the habitable exoplanets are located at small angular separations from their host stars, where the current control laws of XAO systems leave strong residuals. Aims. Current AO control strategies such as static matrix-based wavefront reconstruction and integrator control suffer from a temporal delay error and are sensitive to mis-registration, that is, to dynamic variations of the control system geometry. We aim to produce control methods that cope with these limitations, provide a significantly improved AO correction, and, therefore, reduce the residual flux in the coronagraphic point spread function (PSF). Methods. We extend previous work in reinforcement learning for AO. The improved method, called the Policy Optimization for Adaptive Optics (PO4AO), learns a dynamics model and optimizes a control neural network, called a policy. We introduce the method and study it through numerical simulations of XAO with Pyramid wavefront sensor (PWFS) for the 8-m and 40-m telescope aperture cases. We further implemented PO4AO and carried out experiments in a laboratory environment using Magellan Adaptive Optics eXtreme system (MagAO-X) at the Steward laboratory.Results. PO4AO provides the desired performance by improving the coronagraphic contrast in numerical simulations by factors of 3-5 within the control region of deformable mirror and PWFS, both in simulation and in the laboratory. The presented method is also quick to train, that is, on timescales of typically 5-10 seconds, and the inference time is sufficiently small (< ms) to be used in real-time control for XAO with currently available hardware even for extremely large telescopes.
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