Review for NeurIPS paper: Interferobot: aligning an optical interferometer by a reinforcement learning agent

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.