Reinforcement Learning for Plasma Control: A Proof of Concept for NTM Suppression

Neoclassical tearing modes (NTMs) are magnetohydrodynamic instabilities that generate magnetic islands in tokamak plasmas, degrading confinement and potentially limiting high-performance operation. Their stabilization typically requires precise alignment and appropriate injection of electron cyclotron (EC) power beams, making real-time control a challenging task. In this work, we present a proof-of-principle study aimed at investigating the potential role of neural networks in the control of plasma instabilities. The objective is not the design of a controller for a specific machine, but rather to study how a learning-based agent can autonomously discover effective stabilization strategies through reinforcement learning. A synthetic environment based on a tokamak scenario is used as a test bed for this investigation; the specific scenario plays no essential role in the methodological conclusions. The controller is trained using reinforcement learning techniques and operates solely on a representation of the magnetic island width, without relying on equilibrium reconstruction or explicit knowledge of the deposition location relative to the island. Two control tasks are considered: pure angular alignment and combined angular alignment with power control. The strategies that autonomously emerge are consistent with hand-designed approaches reported in the literature, while the framework remains flexible for incorporating additional objectives such as power minimization. This exploratory study establishes a framework for assessing the potential advantages of data-driven approaches in magnetic island control and provides a basis for future investigations aimed at improving alignment and suppression strategies in fusion plasmas.