Deployable Reinforcement Learning for Interferometric Control

About

Gravitational-wave detectors present a difficult control problem. They are strongly coupled, nonstationary, and safety-critical, and there is very little room to learn directly on real hardware. In this talk, I will describe a practical approach to reinforcement learning for interferometric sensing and control, drawing on lessons from both kilometer-scale interferometers and tabletop optical testbeds. I will focus on building useful observations from the underlying physics, using scalable simulations and reduced-order models to make training practical, reuse of existing hardware data, and adding supervisory checks for safer online operation. The broader aim is not to replace classical control, but to combine it with search and learning in a way that is more adaptive and more data-efficient for precision optical systems.

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