An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
Abstract
Deep reinforcement learning has the potential to address various scientific problems. In this paper, we implement an optics simulation environment for reinforcement learning based controllers. The environment captures the essence of nonconvexity, nonlinearity, and time-dependent noise inherent in optical systems, offering a more realistic setting. Subsequently, we provide the benchmark results of several reinforcement learning algorithms on the proposed simulation environment. The experimental findings demonstrate the superiority of off-policy reinforcement learning approaches over traditional control algorithms in navigating the intricacies of complex optical control environments.
Cite
Text
Abuduweili and Liu. "An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms." Transactions on Machine Learning Research, 2023.Markdown
[Abuduweili and Liu. "An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms." Transactions on Machine Learning Research, 2023.](https://mlanthology.org/tmlr/2023/abuduweili2023tmlr-optical/)BibTeX
@article{abuduweili2023tmlr-optical,
title = {{An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms}},
author = {Abuduweili, Abulikemu and Liu, Changliu},
journal = {Transactions on Machine Learning Research},
year = {2023},
url = {https://mlanthology.org/tmlr/2023/abuduweili2023tmlr-optical/}
}