Adaptive Optics control using Model-Based Reinforcement Learning

TL;DR

This paper introduces a model-based reinforcement learning approach for adaptive optics control in astronomy, demonstrating its ability to adapt to turbulence and calibration errors in simulations.

Contribution

It formulates AO control as a model-based RL problem and shows its effectiveness in predicting turbulence and handling calibration issues in simulated AO systems.

Findings

Predicts turbulence evolution accurately

Adapts to mis-registration between components

Learns continuously on short timescales

Abstract

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.