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.

中文翻译：

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使用基于模型的强化学习进行自适应光学控制

强化学习（RL）提出了一种用于控制天文学自适应光学（AO）系统的新方法。它有望有效地应对经常妨碍AO性能的某些方面，例如时间延迟或校准错误。我们将AO控制回路公式化为基于模型的RL问题（MBRL），并将其应用于数值模拟中的基于简单Shack-Hartmann传感器（SHS）的AO系统中，该系统在整个孔径上具有24个分辨率元素。仿真表明，MBRL控制的AO可以预测湍流的时间演变，并调整可变形反射镜和SHS之间的配准误差，这是AO中的典型校准问题。该方法在几秒钟的时间尺度上连续学习，因此能够自动调整以适应不断变化的条件。