 Magnetic control of tokamak plasmas through deep reinforcement learningJonas 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 and
Martin Riedmiller
Nature, 2022, vol. 602, issue 7897, 414-419 Abstract: 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 control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable1,2, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied. Date: 2022 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:nat:nature:v:602:y:2022:i:7897:d:10.1038_s41586-021-04301-9 Ordering information: This journal article can be ordered from DOI: 10.1038/s41586-021-04301-9 Access Statistics for this article Nature is currently edited by Magdalena Skipper More articles in Nature from Nature |
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