Plasma Shape Control via Zero-shot Generative Reinforcement Learning

• URL: http://arxiv.org/abs/2510.17531v1
• Date: Mon, 20 Oct 2025 13:34:51 GMT
• Title: Plasma Shape Control via Zero-shot Generative Reinforcement Learning
• Authors: Niannian Wu, Rongpeng Li, Zongyu Yang, Yong Xiao, Ning Wei, Yihang Chen, Bo Li, Zhifeng Zhao, Wulyu Zhong,
• Abstract summary: We develop a novel framework for developing a versatile, zero-shot control policy from a large-scale offline dataset of PID-controlled discharges.<n>The resulting foundation policy can be deployed for diverse trajectory tracking tasks in a zero-shot manner without any task-specific fine-tuning.
• Score: 17.3934551430283
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional PID controllers have limited adaptability for plasma shape control, and task-specific reinforcement learning (RL) methods suffer from limited generalization and the need for repetitive retraining. To overcome these challenges, this paper proposes a novel framework for developing a versatile, zero-shot control policy from a large-scale offline dataset of historical PID-controlled discharges. Our approach synergistically combines Generative Adversarial Imitation Learning (GAIL) with Hilbert space representation learning to achieve dual objectives: mimicking the stable operational style of the PID data and constructing a geometrically structured latent space for efficient, goal-directed control. The resulting foundation policy can be deployed for diverse trajectory tracking tasks in a zero-shot manner without any task-specific fine-tuning. Evaluations on the HL-3 tokamak simulator demonstrate that the policy excels at precisely and stably tracking reference trajectories for key shape parameters across a range of plasma scenarios. This work presents a viable pathway toward developing highly flexible and data-efficient intelligent control systems for future fusion reactors.

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