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And the data-efficiency of that process is staggering -- a human only needs a handful of games to come up with mental abstractions enabling it to programmatically solve most of these games at 100%. That's what the bar is. That's what we should aim for.
Don't let them tell you that deep learning has achieved "superhuman" performance at any of these games -- any random programmer can come up with a better solution program for a given game in an afternoon. That's what human-level means.
Did someone claim "superhuman" performance in these games? I thought it was only for AlphaGo (where it is legitimate).
Games are made for humans, not algorithms, thus they are based on prior human knowledge which algorithms usually dont have. There is interesting paper on this: https://rach0012.github.io/humanRL_website/ …
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