I'm a PhD student in robotics at Carnegie Mellon working on exactly this. It's extremely challenging for a few reasons:

- the dataset is a mess. The experiments that have been conducted on the tokamak that we have access to were done for very many different reasons and under many different configurations of the machine so there is not a clear method for disambiguating what dynamical changes are due to differences in the system vs underlying dynamical truths

- the simulators available are very slow and not that accurate

- the physics is hard enough that it's not possible to develop a controller in closed form (obviously)

This implies that we need a version of reinforcement learning or model-predictive control that is substantially more robust and sample-efficient than currently exists. We're working on that but obviously it's an open research problem.