Journal of the Acoustical Society of America
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.
Tristan Shah, Linwei Zhuo, Peter Lai, Amaris De La Rosa-Moreno, Feruza Amirkulova, and Peter Gerstoft. "Reinforcement learning applied to metamaterial design" Journal of the Acoustical Society of America (2021): 321-338. https://doi.org/10.1121/10.0005545
This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Journal of the Acoustical Society of America, Volume 150, Issue 1, Pages 321-338, 2021 and may be found at https://doi.org/10.1121/10.0005545.