Suppressing Modulation Instability with Reinforcement Learning
Pattern Formation and Solitons
2024-07-24 v1 Artificial Intelligence
Machine Learning
Systems and Control
Systems and Control
Applied Physics
Abstract
Modulation instability is a phenomenon of spontaneous pattern formation in nonlinear media, oftentimes leading to an unpredictable behaviour and a degradation of a signal of interest. We propose an approach based on reinforcement learning to suppress the unstable modes by optimizing the parameters for the time modulation of the potential in the nonlinear system. We test our approach in 1D and 2D cases and propose a new class of physically-meaningful reward functions to guarantee tamed instability.
Cite
@article{arxiv.2404.04310,
title = {Suppressing Modulation Instability with Reinforcement Learning},
author = {Nikolay Kalmykov and Rishat Zagidullin and Oleg Rogov and Sergey Rykovanov and Dmitry V. Dylov},
journal= {arXiv preprint arXiv:2404.04310},
year = {2024}
}