Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
Offline reinforcement learning offers a promising route for developing plasma controllers from historical tokamak data, but progress is hindered by the lack of a standardized benchmark. This paper introduces RL4F, an offline RL benchmark for plasma control based on real DIII-D tokamak discharge data, covering four full-profile tracking tasks: rotation, density, temperature, and pressure. Evaluation shows that offline model-based RL methods achieve the best average performance on most objectives, though no single method dominates all tasks, highlighting the importance of dynamics modeling. The codebase, datasets, and evaluation framework are open-sourced.
[2606.07550] Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
[Submitted on 19 May 2026]
Title:Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark
View a PDF of the paper titled Offline Reinforcement Learning for Plasma Control in Nuclear Fusion: Codebase and Benchmark, by Yang Fu and 6 other authors
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Abstract: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 Learning Benchmark for Plasma Control in Nuclear Fusion, providing closed-loop evaluation environments and baseline comparisons across four full-profile tracking tasks: rotation, density, temperature, and pressure. The dynamics function underlying the evaluation environment is built from historical discharge data from DIII-D, a real-world Tokamak. We evaluate a broad set of imitation learning and offline RL baselines under a unified protocol. We find that offline model-based RL methods obtain the best average performance on most objectives, although no single method dominates all tasks, highlighting the importance of dynamics modeling in complex, long-horizon plasma control tasks. To foster further research, we open-source the codebase, datasets, and evaluation framework, providing a benchmark not only for the fusion community but also for algorithm development in offline RL.
Comments: 23 pages (10 pages main text)
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07550 [cs.LG]
(or arXiv:2606.07550v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.07550
arXiv-issued DOI via DataCite
Submission history
From: Haomin Bao [view email] [v1] Tue, 19 May 2026 14:09:29 UTC (9,431 KB)
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