Google DeepMind Taught AI to Control a Nuclear Fusion Reactor in Real Time

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Authors:

  • Jonas Degrave
  • Federico Felici
  • Jonas Buchli
  • Michael Neunert
  • Brendan Tracey
  • Francesco Carpanese
  • Timo Ewalds
  • Roland Hafner
  • Abbas Abdolmaleki
  • Diego de las Casas
  • Craig Donner
  • Leslie Fritz
  • Cristian Galperti
  • Andrea Huber
  • James Keeling
  • Maria Tsimpoukelli
  • Jackie Kay
  • Antoine Merle
  • Jean-Marc Moret
  • Seb Noury
  • Federico Pesamosca
  • David Pfau
  • Olivier Sauter
  • Cristian Sommariva
  • Stefano Coda
  • Basil Duval
  • Ambrogio Fasoli
  • Pushmeet Kohli
  • Koray Kavukcuoglu
  • Demis Hassabis
  • Martin Riedmiller

Abstract

Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. 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. 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 architecture meets...

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