Offline Reinforcement Learning for Rotation Profile Control in Tokamaks
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917467015610368 |
|---|---|
| author | Sonker, Rohit Kaga, Hiro Josep Farre Chen, Jiayu Rothstein, Andrew Char, Ian Shousha, Ricardo Kolemen, Egemen Schneider, Jeff |
| author_facet | Sonker, Rohit Kaga, Hiro Josep Farre Chen, Jiayu Rothstein, Andrew Char, Ian Shousha, Ricardo Kolemen, Egemen Schneider, Jeff |
| contents | Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challenge is controlling the plasma rotation profile, which strongly influences stability, confinement, and transport. While the average rotation can be controlled, controlling the full profile is challenging due to high dimensionality, response to multiple actuators and dependence on plasma condition. Learning-based control methods, such as reinforcement learning (RL), provide a potential solution to this challenging problem with ability to model complex interactions leading to effective multi-input multi-output control. However, learning such policies is challenging due to the lack of accurate simulators that can model the rotation profile dynamics. In this work, we investigate the use of offline RL and offline model-based RL algorithms for rotation profile control, training them solely on historical data from the DIII-D tokamak. Our final method uses probabilistic models of plasma dynamics to generate rollouts for RL training. We deploy this policy on the DIII-D Tokamak and observe promising real-world results. We conclude by highlighting key challenges and insights from training and deploying an RL policy on a complex physical device while using only limited past data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_05857 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Offline Reinforcement Learning for Rotation Profile Control in Tokamaks Sonker, Rohit Kaga, Hiro Josep Farre Chen, Jiayu Rothstein, Andrew Char, Ian Shousha, Ricardo Kolemen, Egemen Schneider, Jeff Machine Learning Tokamaks remain leading candidates for achieving practical fusion energy, yet many important control problems inside these devices are still difficult or unsolved. One such challenge is controlling the plasma rotation profile, which strongly influences stability, confinement, and transport. While the average rotation can be controlled, controlling the full profile is challenging due to high dimensionality, response to multiple actuators and dependence on plasma condition. Learning-based control methods, such as reinforcement learning (RL), provide a potential solution to this challenging problem with ability to model complex interactions leading to effective multi-input multi-output control. However, learning such policies is challenging due to the lack of accurate simulators that can model the rotation profile dynamics. In this work, we investigate the use of offline RL and offline model-based RL algorithms for rotation profile control, training them solely on historical data from the DIII-D tokamak. Our final method uses probabilistic models of plasma dynamics to generate rollouts for RL training. We deploy this policy on the DIII-D Tokamak and observe promising real-world results. We conclude by highlighting key challenges and insights from training and deploying an RL policy on a complex physical device while using only limited past data. |
| title | Offline Reinforcement Learning for Rotation Profile Control in Tokamaks |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2605.05857 |