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.

Limitations in acquiring training data restrict potential applications of deep reinforcement learning (RL) methods to the training of real-world robots.

Summary and Contributions: The paper proposed a learning system for applying deep reinforcement learning algorithms to an optical interferometer aligning task. To achieve successful learning, the authors first developed a simulation tool for simulating interferometric patterns captured by the camera, which is used to train the DRL agents using dueling ddqn. To enable the controller to transfer to the real-world system, they apply domain randomization during training. They demonstrated successful learning of an agent that can perform interferometer aligning tasks and outperforms human operators. Tha main contribution of the paper is that it demonstrates successful application to a new domain of tasks and achieves human-level performance.

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.

A well executed application of deep RL to an interesting and practical problem.

Reinforcement learning is finding its way to real-world problem application, transferring from simulated environments to physical setups. In this work, we implement vision-based alignment of an optical Mach-Zehnder interferometer with a confocal telescope in one arm, which controls the diameter and divergence of the corresponding beam. We use a continuous action space; exponential scaling enables us to handle actions within a range of over two orders of magnitude. Our agent trains only in a simulated environment with domain randomizations. In an experimental evaluation, the agent significantly outperforms an existing solution and a human expert.