Offline Model-Based Reinforcement Learning for Tokamak Control

Ian Char, Joseph Abbate, László Bardóczi, Mark D. Boyer, Youngseog Chung, Rory Conlin, Keith Erickson, Viraj Mehta, Nathan Richner, Egemen Kolemen, Jeff Schneider

Research output: Contribution to journalConference articlepeer-review

3 Scopus citations


Control for tokamaks, the leading candidate technology for nuclear fusion, is an important pursuit since the realization of nuclear fusion as an energy source would result in virtually unlimited clean energy. However, control of these devices remains a challenging problem due to complex, non-linear dynamics. At the same time, there is promise in learning controllers for difficult problems thanks to recent algorithmic developments in reinforcement learning. Because every run (or shot) of the tokamak is extremely expensive, in this work, we investigated learning a controller from logged data before testing it on a tokamak. In particular, we used 18 years of data from the DIII-D device in order to learn a controller for the neutral beams that targets specified βN (normalized ratio of plasma pressure to magnetic pressure) and rotation quantities. This was done by using the data to first learn a dynamics model, and then by using this model as a simulator to generate experience to train a controller via reinforcement learning. During a control session on DIII-D, we tested both the ability for our dynamics model to design feedforward trajectories and the controller's ability to do feedback control to achieve specified targets. This work marks some of the first steps in doing reinforcement learning for tokamak control through historical data alone.

Original languageEnglish (US)
Pages (from-to)1357-1372
Number of pages16
JournalProceedings of Machine Learning Research
StatePublished - 2023
Event5th Annual Conference on Learning for Dynamics and Control, L4DC 2023 - Philadelphia, United States
Duration: Jun 15 2023Jun 16 2023

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Software
  • Control and Systems Engineering
  • Statistics and Probability


  • Model-Based Reinforcement Learning
  • Reinforcement Learning
  • Tokamak Control


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