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research article

Towards practical reinforcement learning for tokamak magnetic control

Tracey, Brendan D.
•
Michi, Andrea
•
Chervonyi, Yuri
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January 23, 2024
Fusion Engineering And Design

Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinement. In this work, we address key drawbacks of the RL method; achieving higher control accuracy for desired plasma properties, reducing the steady-state error, and decreasing the required time to learn new tasks. We build on top of Degrave et al. (2022), and present algorithmic improvements to the agent architecture and training procedure. We present simulation results that show up to 65% improvement in shape accuracy, achieve substantial reduction in the long-term bias of the plasma current, and additionally reduce the training time required to learn new tasks by a factor of 3 or more. We present new experiments using the upgraded RL-based controllers on the TCV tokamak, which validate the simulation results achieved, and point the way towards routinely achieving accurate discharges using the RL approach.

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Type
research article
DOI
10.1016/j.fusengdes.2024.114161
Web of Science ID

WOS:001167503900001

Author(s)
Tracey, Brendan D.
Michi, Andrea
Chervonyi, Yuri
Davies, Ian
Paduraru, Cosmin
Lazic, Nevena
Felici, Federico  
Ewalds, Timo
Donner, Craig
Galperti, Cristian  
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Corporate authors
TCV Team
Date Issued

2024-01-23

Publisher

Elsevier Science Sa

Published in
Fusion Engineering And Design
Volume

200

Article Number

114161

Subjects

Technology

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Magnetic Control

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Tokamak Control

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Reinforcement Learning

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Transfer Learning

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Tokamak Experimental Comparison

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
SPC  
FunderGrant Number

Kieran Milan

Swiss National Science Foundation

Euratom Research and Training Programme

101052200

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Available on Infoscience
March 18, 2024
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/206494
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