<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>317070</author><text>Hi, author here. We validated the neural network controller on the real plant in a couple of experiments. All experiments in the paper are based on data from running the controller on an actual tokamak.&lt;p&gt;There is a video of one of these experiments in the linked blogpost (figure 2), where we show imagery taken from a camera looking into the vessel. Next to it is a reconstruction of the shot, visualised on a cross-section of the torus.&lt;p&gt;The simulation not perfectly matching reality is indeed a major challenge, and the nature paper is mainly about showing how you can overcome that challenge. As others have said, plasma is a pain to model, and you cannot collect a ton of data on these machines. But it seems technology is coming at a point where we know how to work within these limits and still get it to work.</text><parent_chain><item><author>klabb3</author><text>AFAIU this is entirely simulated - if it turns out the simulation doesn&amp;#x27;t perfectly match reality, what are the challenges in re-adjusting? Does the RL network need to be re-trained with massive amounts of data on a new improved simulation or can it be fine tuned on the real thing?</text></item></parent_chain></comment><story><title>Controlling the nuclear fusion plasma in a tokamak with reinforcement learning</title><url>https://deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-control</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>willis936</author><text>This is why fundamental plasma physics is so valuable to fund.  ML can come up with good models that fit the data in the domains that there are empirical datasets to train against, but it can&amp;#x27;t reliably extrapolate outside of those domains.&lt;p&gt;So this is a good tool for the computationalists but we still need the experimentalists.  This is science.</text><parent_chain><item><author>klabb3</author><text>AFAIU this is entirely simulated - if it turns out the simulation doesn&amp;#x27;t perfectly match reality, what are the challenges in re-adjusting? Does the RL network need to be re-trained with massive amounts of data on a new improved simulation or can it be fine tuned on the real thing?</text></item></parent_chain></comment><story><title>Controlling the nuclear fusion plasma in a tokamak with reinforcement learning</title><url>https://deepmind.com/blog/article/Accelerating-fusion-science-through-learned-plasma-control</url></story>