Interferobot: aligning an optical interferometer by a reinforcement learning agent

Part of Advances in Neural Information Processing Systems 33 (NeurIPS 2020)

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Authors

Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky

Abstract

Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots. Here we train an RL agent to align a Mach-Zehnder interferometer, which is an essential part of many optical experiments, based on images of interference fringes acquired by a monocular camera. The agent is trained in a simulated environment, without any hand-coded features or a priori information about the physics, and subsequently transferred to a physical interferometer. Thanks to a set of domain randomizations simulating uncertainties in physical measurements, the agent successfully aligns this interferometer without any fine-tuning, achieving a performance level of a human expert.