Finally someone beat Montezuma's Revenge without imitating human demonstrations! Very cool. I wonder why the algorithm then fails so hard on Pitfall? I would expect them to be similar problems.
Just to add a bit about the point of the "AI winter reality check" X "Stephen Hawkings hype", I think both are right and wrong at the same time: I do believe that the analisis of the human reasoning and its simulation in computers can be achieved with much less computing power than that needed by most insects sensorial systems(so in this sense agree with Moravec, but...). The computing power can accelerate learning in a system, but its a question of time and nothing more, it doesnt matter really if you take a week or a year to "set-train-load" a reasoning human level AI, the achievement would be amazing anyway. So is the mistake, in my opinion, of all the wave-of-hype's direction: neural nets is just for acceleration and generation of datasets(what you say!? yes, generation, thats what they are really for, totally contrarilly to the world's opinion : ) !!! ) and , obviously for the "lesser utility" of sensorial system autonomous programming, that is what they are using it for right now, but it have nothing to do with "Reasoning Intelligence", that would be, in ma humble opinion, lets say, a programm that could take part and give some insigthful contribution in the famous talk between Albert Einstein and Rabindranath Tagore.[1] That said, I do belive a non-neural net "real deal" AI is just around the corner, not that far, really, and here maybe I do tend a bit to the "Hawkings-hype" people, but completely differently from them, I do believe it has nothing to do with neural nets(that are just tools), and are...lets say: "non-neural networks heuristic based".(In this case, if an agent can beat 'human performance' by only clearing 1 of 9 total levels, one is entitled to a little skepticism about how useful 'human performance' is as a benchmark for this particular game. Focus on the improvement over other DRL agents, not that.)
For example, speech recognition AI is supposedly within fraction of a percent from "average human level", and yet auto-generated captions are awful. They have no punctuation, they don't distinguish between different speakers, they aren't visually grouped, and fail miserably dealing with slang. So turns out researchers are measuring only one aspect of the problem their algorithm is good at and ignoring the rest.
On the flip side, we have animal intelligence. Bees aren't nearly as smart as humans. So surely modern AI, which surpasses humans in this and that, would have no problem outperforming a bee with its 960 000 neurons, right? But in reality, there is nothing even approaching bees' versatile intelligence. Of course, modern AI researchers would just hand-wave this saying the problem is not well defined. Convenient.
YouTube captioning != SOTA, any more than Google Translate for years and years represented anything close to the NMT SOTA.