强化学习自寻优自适应光学控制
Self-optimizing adaptive optics control with Reinforcement Learning
摘要：
当前和未来的高对比度成像仪器需要极端的自适应光学（XAO）系统来达到直接对系外行星成像所需的对比度。望远镜的振动和由控制回路的延迟引起的时间误差限制了这些系统的性能。（预测）控制算法的优化是减少这些影响的关键。我们描述了无模型强化学习如何用于优化闭环自适应光学控制的递归神经网络控制器。我们验证了我们提出的方法在模拟和实验室设置的倾斜倾斜控制。结果表明，该算法可以有效地学习，以抑制倾斜的倾斜振动的组合。此外，我们报告减少残差幂律输入湍流相比，一个最佳的增益积分。最后，我们证明了控制器可以学习识别参数的变化的振动，而不需要在线更新的控制律。我们的结论是，强化学习是一种很有前途的方法，数据驱动的预测控制，未来的研究将应用这种方法来控制高阶变形镜
英文摘要：
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 approach for tip-tilt control in simulations and a lab setup. The results show that this algorithm can effectively learn to suppress a combination of tip-tilt vibrations. Furthermore, we report decreased residuals for power-law input turbulence compared to an optimal gain integrator. Finally, we demonstrate that the controller can learn to identify the parameters of a varying vibration without requiring online updating of the control law. We conclude that Reinforcement Learning is a promising approach towards data-driven predictive control; future research will apply this approach to the control of high-order deformable mirrors
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