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<p>Reinforcement learning (RL)-based control in tokamaks offers improved flexibility for nuclear fusion, but typically depends on simulators that can accurately evolve the high-dimensional plasma state. First-principle simulators are often too computationally intensive for efficient RL training. Here, we develop a fully data-driven simulator that mitigates compounding errors caused by its autoregressive nature. This high-fidelity model enables rapid training of an RL agent that generates engineering-reasonable actuator commands to reach long-term plasma configuration targets. Combined with a neural network surrogate for equilibrium fitting (EFITNN), the agent maintains a 400-ms, 1 kHz control trajectory on the HL-3 tokamak, accurately tracking plasma current and boundary shape. It also adapts to changes in triangularity without retraining, demonstrating robustness. These results show that data-driven dynamics models can support fast and reliable RL-based control, meeting anticipated engineering requirements for routine operation in future fusion devices such as ITER.</p>

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High-fidelity data-driven dynamics model for reinforcement learning-based control in HL-3 tokamak

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月3日

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  • Niannian Wu,
  • Zongyu Yang,
  • Rongpeng Li,
  • Ning Wei,
  • Yihang Chen,
  • Qianyun Dong,
  • Jiyuan Li,
  • Guohui Zheng,
  • Xinwen Gong,
  • Feng Gao,
  • Bo Li,
  • Min Xu,
  • Zhifeng Zhao,
  • Wulyu Zhong

摘要

Reinforcement learning (RL)-based control in tokamaks offers improved flexibility for nuclear fusion, but typically depends on simulators that can accurately evolve the high-dimensional plasma state. First-principle simulators are often too computationally intensive for efficient RL training. Here, we develop a fully data-driven simulator that mitigates compounding errors caused by its autoregressive nature. This high-fidelity model enables rapid training of an RL agent that generates engineering-reasonable actuator commands to reach long-term plasma configuration targets. Combined with a neural network surrogate for equilibrium fitting (EFITNN), the agent maintains a 400-ms, 1 kHz control trajectory on the HL-3 tokamak, accurately tracking plasma current and boundary shape. It also adapts to changes in triangularity without retraining, demonstrating robustness. These results show that data-driven dynamics models can support fast and reliable RL-based control, meeting anticipated engineering requirements for routine operation in future fusion devices such as ITER.