论文标题
基准增强学习算法的光学控制环境
An Optical Control Environment for Benchmarking Reinforcement Learning Algorithms
论文作者
论文摘要
深度强化学习有可能解决各种科学问题。在本文中，我们为基于增强学习的控制器实施了光学模拟环境。环境捕获了非概念性，非线性和与时间相关的噪声的本质，从而提供了更现实的设置。随后，我们提供了有关拟议的模拟环境中几种增强学习算法的基准结果。实验发现表明，在导航复杂光学控制环境的复杂性时，非政策加强学习方法比传统控制算法的优越性。该论文的代码可在https://github.com/walleclipse/reinforection-learning-pulse-actacking上找到。
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.
