Magnetic control of tokamak plasmas through deep reinforcement learning

Abstract Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel.

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• 1 A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel.
• 2 This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations.
• 3 In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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