Interferobot: aligning an optical interferometer by a reinforcement learning agent
Authors: Dmitry Sorokin, Alexander Ulanov, Ekaterina Sazhina, Alexander Lvovsky
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Academia | 1Russian Quantum Center, Moscow, Russia 2University of Oxford, United Kingdom 3P. N. Lebedev Physics Institute, Moscow, Russia 4Moscow Institute of Physics and Technology |
| Pseudocode | No | The paper describes the method in prose and mathematical equations but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | Videos of the interferometer alignment and software are available via link https://github.com/ dmitry Sorokin/interferobot Project |
| Open Datasets | No | The paper uses data generated by its own simulator for training, but does not provide concrete access information (link, DOI, etc.) to a pre-existing or archived public dataset. |
| Dataset Splits | No | The paper describes training and evaluation episodes, but it does not specify traditional train/validation/test dataset splits with percentages or sample counts for a static dataset. Data is generated dynamically within a simulated environment for training. |
| Hardware Specification | Yes | The whole training on a NVidia GTX 2060 GPU took about 10 hours. |
| Software Dependencies | No | The paper mentions software components like 'gym-like interface' and 'parallel C++ code' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We train the agent in the simulated environment using the double dueling DQN algorithm [31] with a discount factor γ = 0.99, total number of steps 5 106, and replay buffer size 3 104. Updates were performed every four steps. |