Reinforcement Learning Controllers for Fusion Scenario Development
Scenario development, the design of the waveforms that take a tokamak discharge from breakdown to flat-top, is still an iterative, labour-intensive process. Every candidate scenario has to be simulated with a controller in the loop, and the classical shape and current controllers need low-level actuator references. So the process is iterative and includes a number of trial-and-error steps. We propose to replace the per-scenario controller tuning by a single goal-conditioned reinforcement-learning controller trained once, in advance, against a free-boundary equilibrium and transport simulator. A scenario is then specified only by the target waveforms of the physical quantities of interest ...
| Authors | Evgeny Adishchev, Maxim Nurgaliev, Dmitry Sorokin et al. |
|---|---|
| Primary category | physics.plasm-ph |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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