mlsanityData to 14 Oct 2026

Suppressing Modulation Instability with Reinforcement Learning

arXiv 2404.04310 ↗First posted 5 Apr 2024nlin.PS cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL theory and sample complexity

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL theory and sample complexity, one of 15 directions in Deep reinforcement learning methods. Builds on 9 of its 49 references that are in the map. First posted on arXiv 5 Apr 2024.

Builds on

9 of 49 references.

References · 9

  1. 2019DeepXDE: A deep learning library for solving differential equationscited by 619
  2. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  3. 2017Deep learning for universal linear embeddings of nonlinear dynamicscited by 494
  4. 2018Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equationscited by 235
  5. 2017Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equationscited by 208
  6. 2019Control of chaotic systems by Deep Reinforcement Learningcited by 29
  7. 2019'Zhores' -- Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technologycited by 23
  8. 2018Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Controlcited by 11
  9. 2020Reinforcement Learning Framework for Deep Brain Stimulation Studycited by 6

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  4. 2025Multi-fidelity Reinforcement Learning Control for Complex Dynamical Systems
  5. 2025Wavelet Fourier Diffuser: Frequency-Aware Diffusion Model for Reinforcement Learning
  6. 2025Reinforcement learning for spin torque oscillator tasks
  7. 2026On the Convergence of Semi-Gradient TD: A Mean Field Perspective
  8. 2025VARD: Efficient and Dense Fine-Tuning for Diffusion Models with Value-based RL