Reinforcement Learning for Lock Acquisition of the LIGO 40-Meter Interferometer

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Abstract:: The current locking scheme at the LIGO 40-Meter Interferometer uses closed control loops to guard against noise and acquire lock. However, this linear control method is inefficient and time-consuming for a nonlinear system. In this paper, we present reinforcement learning as an alternative approach for lock acquisition, known as intelligent control. We first develop a neural network simulation of FINESSE 3, an interferometer modeling software, achieving a significant increase in simulation speed at the expense of some accuracy. This simulation is evolved in time with the noise forces present in the 40-meter laboratory. We then train a Proximal Policy Optimization (PPO) agent to acquire lock in the simulated environment. This work is particularly relevant as future upgrades in laser power and interferometer complexity are expected to increase the frequency of lock loss. Intelligent control can reduce detector downtime, and our research lays the groundwork for a prototype implementation at the 40m interferometer.

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LIGO Document T2400293-v1