To solve the global energy crisis, researchers have long sought a source of clean, limitless energy. Nuclear fusion, the reaction that powers the stars of the universe, is one contender. By smashing and fusing hydrogen, a common element of seawater, the powerful process releases huge amounts of energy. Here on earth, one way scientists have recreated these extreme conditions is by using a tokamak, a doughnut-shaped vacuum surrounded by magnetic coils, that is used to contain a plasma of hydrogen that is hotter than the core of the Sun. However, the plasmas in these machines are inherently unstable, making sustaining the process required for nuclear fusion a complex challenge. For example, a control system needs to coordinate the tokamak's many magnetic coils and adjust the voltage on them thousands of times per second to ensure the plasma never touches the walls of the vessel, which would result in heat loss and possibly damage. To help solve this problem and as part of DeepMind’s mission to advance science, we collaborated with the Swiss Plasma Center at EPFL to develop the first deep reinforcement learning (RL) system to autonomously discover how to control these coils and successfully contain the plasma in a tokamak, opening new avenues to advance nuclear fusion research.