text:   high-contrast imaging",6,"['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. https://t.co/PpTQbD4jH4', '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. https://t.co/rtMXovY5T8', 'We also validate our control algorithm in the lab for tip-tilt control and effectively show that this can remove vibrations from the system. https://t.co/uAAX9s6euC', '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.']",21,08,1254