Suppressing Modulation Instability with Reinforcement Learning
This addresses signal degradation in nonlinear systems for physics and engineering applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of modulation instability in nonlinear media, which degrades signals, by using reinforcement learning to optimize potential modulation parameters, achieving suppression of unstable modes in 1D and 2D cases with new reward functions.
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.