Suppressing Modulation Instability with Reinforcement Learning
Nikolay Kalmykov Rishat Zagidullin Oleg Rogov Sergey Rykovanov Dmitry V. Dylov
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.