Tokamak

Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark

Offline reinforcement learning offers a promising path for developing plasma controllers from historical tokamak data. This paper introduces RL4F, a benchmark for offline reinforcement learning in nuclear fusion plasma control, evaluating various baselines and finding that model-based RL methods perform best.