text: 346,30,1432260914095460352,1326269340493246467,Rico Landman,"Happy to announce the publication of my first master project in @SPIEtweets JATIS on a new approach towards data-driven predictive control for high-contrast imaging. Find the paper on <LINK> or <LINK>. <LINK> Our approach for predictive control is based on Reinforcement Learning, a subfield of Machine Learning that takes inspiration from the way humans and animals learn. The general idea is that the adaptive optics system learns to optimize a “reward” through trial and error. This can be framed into a mathematical optimization problem. We can then try to estimate the solution using numerical optimization methods, such as gradient descent. <LINK> We show that our approach can lead to &gt;order of magnitude improvement close to the star in a simulated adaptive optics system, even under non-stationary wind speeds and directions. <LINK> We also validate our control algorithm in the lab for tip-tilt control and effectively show that this can remove vibrations from the system. <LINK> Our approach allows for a flexible framework to optimize nonlinear data-driven controllers. In principle, it is also possible to feed it with information from multiple sensors and directly optimize for nonlinear objectives, e.g. the contrast in the focal plane.",https://arxiv.org/abs/2108.11332,"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. 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 control in simulations and a lab setup. The results show that this algorithm can effectively learn to mitigate vibrations and reduce the residuals for power-law input turbulence as compared to an optimal gain integrator. We also show that the controller can learn to minimize random vibrations without requiring online updating of the control law. Next, we show in simulations that our algorithm can also be applied to the control of a high-order deformable mirror. We demonstrate that our controller can provide two orders of magnitude improvement in contrast at small separations under stationary turbulence. Furthermore, we show more than an order of magnitude improvement in contrast for different wind velocities and directions without requiring online updating of the control law. ","Self-optimizing adaptive optics control with Reinforcement Learning for