An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms

ABULIKEMU ABUDUWEILI, Changliu Liu

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.

Submission Length: Regular submission (no more than 12 pages of main content)

Changes Since Last Submission:

1. Add links to the Code and Supplemental Video.
2. Remove the sentence "We will make the code publicly available."

Video: https://github.com/Walleclipse/Reinforcement-Learning-Pulse-Stacking/tree/main/demo

Code: https://github.com/Walleclipse/Reinforcement-Learning-Pulse-Stacking

Assigned Action Editor: ~Amir-massoud_Farahmand1

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Submission Number: 1255