Research by CMU School of Computer Science student marks several firsts in field
A student in Carnegie Mellon University's School of Computer Science (SCS) has used reinforcement learning to help control nuclear fusion reactions, a significant step toward harnessing the immense power produced in nuclear fusion as a source of clean, abundant energy.
Ian Char, a doctoral candidate in the Machine Learning Department, used reinforcement learning to control the hydrogen plasma of the tokamak machine at the DIII-D National Fusion Facility in San Diego. He was the first CMU researcher to run an experiment on the sought-after machines, the first to use reinforcement learning to affect the rotation of a tokamak plasma, and the first person to try reinforcement learning on the largest operating tokamak machine in the United States. Char collaborated with the Princeton Plasma Physics Laboratory (PPPL) on the work.
"Reinforcement learning affected the plasma's pressure and its rotation," Char said. "And that's really our big first here."
