Game-playing AI technique may lead to cheaper nuclear energy

Background: Nuclear energy provides more carbon-free electricity in the United States than solar and wind combined, making it a key player in the fight against climate change. But the U.S. nuclear fleet is aging, and operators are under pressure to streamline their operations to compete with coal- and gas-fired plants.

One of the key places to cut costs is deep in the reactor core, where energy is produced. If the fuel rods that drive reactions there are ideally placed, they burn less fuel and require less maintenance. Through decades of trial and error, nuclear engineers have learned to design better layouts to extend the life of pricey fuel rods.

Deep learning: The researchers wondered if deep reinforcement learning, an AI technique that has achieved superhuman mastery at games like chess and Go, could make the layout iteration process faster. Deep reinforcement learning combines deep neural networks, which excel at picking out patterns in reams of data, with reinforcement learning, which ties learning to a reward signal like winning a game, as in Go, or reaching a high score, as in Super Mario Bros.

Wide application: “This technology can be applied to any nuclear reactor in the world,” says the study’s senior author, Koroush Shirvan, an assistant professor in MIT’s Department of Nuclear Science and Engineering. “By improving the economics of nuclear energy, which supplies 20 percent of the electricity generated in the United States, we can help limit the growth of global carbon emissions and attract the best young talents to this important clean-energy sector.”