強化学習アルゴリズム評価のための光学制御シミュレーション環境
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
光学系の非凸性・非線形性・時間依存ノイズを再現した強化学習向けシミュレーション環境を構築し、複数のRLアルゴリズムをベンチマークしてオフポリシー手法の優位性を示した。
著者: Abulikemu Abuduweili, Changliu Liu
分類: cs.LG, cs.RO, cs.SY, eess.SY
原文アブストラクト
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. The code of the paper is available at https://github.com/Walleclipse/Reinforcement-Learning-Pulse-Stacking.
