Interferobot: aligning an optical interferometer by a reinforcement learning agent

• URL: http://arxiv.org/abs/2006.02252v2
• Date: Thu, 4 Feb 2021 08:35:39 GMT
• Title: Interferobot: aligning an optical interferometer by a reinforcement learning agent
• Authors: Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky
• Abstract summary: We train an RL agent to align a Mach-Zehnder interferometer, based on images of 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. Thanks to a set of domain randomizations simulating uncertainties in physical measurements, the agent successfully aligns this interferometer without any fine tuning.
• Score: 118.43526477102573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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