This paper presents a semi-analytical method of suppressing acoustic scattering using reinforcement learning (RL) algorithms. We give a RL agent control over design parameters of a planar configuration of cylindrical scatterers in water. These design parameters control the position and radius of the scatterers. As these cylinders encounter an incident acoustic wave, the scattering pattern is described by a function called total scattering cross section (TSCS). Through evaluating the gradients of TSCS and other information about the state of the configuration, the RL agent perturbatively adjusts design parameters, considering multiple scattering between the scatterers. As each adjustment is made, the RL agent receives a reward negatively proportional to the root mean square of the TSCS across a range of wavenumbers. Through maximizing its reward per episode, the agent discovers designs with low scattering. Specifically, the double deep Q-learning network and the deep deterministic policy gradient algorithms are employed in our models. Designs discovered by the RL algorithms performed well when compared to a state-of-the-art optimization algorithm using fmincon.

中文翻译：

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强化学习应用于超材料设计

本文提出了一种使用强化学习 (RL) 算法抑制声学散射的半解析方法。我们让 RL 代理控制水中圆柱形散射体的平面配置的设计参数。这些设计参数控制散射体的位置和半径。当这些圆柱体遇到入射声波时，散射模式由称为总散射截面 (TSCS) 的函数描述。通过评估 TSCS 的梯度和有关配置状态的其他信息，RL 代理会微扰调整设计参数，同时考虑散射体之间的多次散射。随着每次调整的进行，RL 代理会收到与 TSCS 的均方根在一系列波数中成负比的奖励。通过最大化每集的奖励，代理发现具有低散射的设计。具体来说，我们的模型中采用了双深度 Q 学习网络和深度确定性策略梯度算法。与最先进的优化算法相比，RL 算法发现的设计表现良好飞明康。