Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark (opens in new tab)  🤖Reinforcement Learning  Content type: Academic

Offline reinforcement learning (RL) offers a promising route for developing plasma controllers from historical tokamak data, since online trial-and-error on real devices is costly and risky. However, progress in this direction remains difficult to measure due to the lack of a standardized offline RL benchmark for realistic multi-actuator, long-horizon plasma control problems in nuclear fusion. We introduce RL4F, an Offline Reinforcement Learni...