Suppressing Modulation Instability with Reinforcement Learning
2024-04-05Code Available0· sign in to hype
Nikolay Kalmykov, Rishat Zagidullin, Oleg Rogov, Sergey Rykovanov, Dmitry V. Dylov
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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.