Unlimited Clean Energy Through Reinforced Nuclear Fusion Reactions Management

Researchers at Carnegie Mellon University recently used reinforcement learning to help control nuclear fusion reactions for the first time, according to research published by DeepMind in the journal Nature. It is a significant step toward harnessing the immense power produced in nuclear fusion as an abundant, clean energy source.

Nuclear fusion occurs when two hydrogen nuclei fuse to form a heavier nucleus, releasing enormous amounts of energy. Using a magnetic field to contain hydrogen plasma at the required temperature and pressure to fuse the outer shells of atomic nuclei is another way to create nuclear fusion. This process generally occurs in a device called Tokamak (TCV, Variable Configuration Tokamak), a large machine that uses a magnetic field to confine a hydrogen plasma in a torus shape. The process requires numerous micromanipulations of the magnetic field and other hydrogen particles to maintain the plasma and its condition.

Reinforcement learning is one of the three basic machine learning types. Study subjects gradually develop expectations of the stimulus under the environment’s incentives or punishments, thereby maximizing the long-term benefits of a series of actions or strategies.

DeepMind, an Artificial Intelligence research arm at Alphabet, Google’s parent company, developed the first reinforcement learning system incorporating nuclear fusion to regulate the magnetic field. The lab succeeded in keeping the plasma stable and forming various shapes. The department ran its experiment on the Variable Configuration Tokamak (TCV) in Lausanne, Switzerland, and published its findings in Nature.