Measured acceleration · Physics
Deep reinforcement learning controls plasma shapes in a research tokamak
Reinforcement-learning plasma-shape control deployed on a research tokamak.
Summary
The Nature paper reports a deep reinforcement-learning architecture for magnetic control of plasmas on the TCV research tokamak. The system learned in simulation and was deployed on hardware to produce and control several plasma configurations, including elongated, negative-triangularity and snowflake shapes.
AI role
Learned control policies in simulation and transferred them to real tokamak hardware.
Narrative role
This event shows AI affecting experimental physics control, not just offline modeling or literature work.
Caveat
The result is a controlled tokamak demonstration and not a solved fusion-energy workflow.