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Completed TRL 3 (started at 1, targeting 3)
We propose developing a Reinforcement Learning (RL) agent that will produce optical wavefront sensing and control algorithms addressing 2 application areas: (a) “digging” optimal coronagraphic dark-holes, and (b) Wavefront Sensing (WFS) based PSF calibration. The ultimate target opportunity for this work is toward the Habitable Worlds Observatory (HWO).
The objective is to capitalize on the latest developments in machine learning (Reinforcement Learning) that uses pattern recognition algorithms to navigate a complex design environment, while maximizing a reward function to produce agents that can provide solutions to digging coronographic dark-holes and WFS-based PSF calibration. The effort constitutes the development of a new and enabling capability.
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