Controlling Fusion Reactor Instability with Deep Reinforcement Learning with Aza Jalalvand - #682

This article discusses the application of deep reinforcement learning (DRL) to control plasma instabilities in nuclear fusion reactors. The focus is on the work of Azarakhsh Jalalvand, a research scholar at Princeton University, who developed a model to detect and mitigate 'tearing mode,' a critical instability. The article highlights the process of data collection, model training, and deployment of the controller algorithm on the DIII-D fusion research reactor. It also touches upon future challenges and opportunities for AI in achieving stable and efficient fusion energy production. The source is a podcast episode from Practical AI.

• •Deep reinforcement learning is being used to control plasma instabilities in nuclear fusion reactors.
• •A model was developed to detect and avoid the 'tearing mode' instability.
• •The controller algorithm was deployed on the DIII-D fusion research reactor.