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His goal was not just to solve Atari games. That was already done.

His goal is to develop generic methods. So you could work with more complex games or the physical world for that, as that is what you want in the end. However, his insight is, you can even modify the Atari setting to test this, e.g. to work in realtime, and the added complexity by more complex games doesn't really give you any new additional insights at this point.

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But how is this different to what NVIDIA have already done? They have robots that can achieve arbitrary and fluid actions in the real world by training NNs in very accurate GPU simulated environments using physics engines. Moving a little Atari stick around seems like not much compared to sorting through your groceries etc.

The approach NVIDIA are using (and other labs) clearly works. It's not going to be more than a year or two now before robotics is as solved as NLP and chatbots are today.

I think he argues that they would not be able to play Atari games this way (I don't know; maybe I also misunderstood).

But also, he argues a lot about sample efficiency. He wants to develop algorithms/methods/models which can learn much faster / with much fewer data.

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