But that's a skill-based game, as opposed to strategy or anything needing intelligence. "Skill" as in reaction time to seeing an opponent and successfully moving clicking the mouse of their head.
Strangely enough, they already thought of that:
First, we noticed that the agents had very fast reaction times and were very accurate taggers, which could explain their performance. However, by artificially reducing this accuracy and reaction time we saw that this was only one factor in their success. ...Even with human-comparable accuracy and reaction time the performance of our agents is higher than that of humans.
Both the summary and the Verge article seem to have missed the point of this development -- an improvement to the agent design scheme.
Last year, after smashing both go and chess with their self-play-from-zero strategy, they tried the same thing with Starcraft. And they lost spectacularly -- even after millions of games, their self-trained DeepMind agents were unable to beat even the most simplistic "scripted" StarCraft AI -- the ones designed for n00b humans to beat up on. They discovered that while the self-play agents were able to eventually figure out activities like "harvest minerals", they were unable to put those together into higher-level activities like building an army and winning a game.
One of the key refinements they introduce in this paper is to allow the agents to evolve their own internal "rewards", which were sub-steps towards winning. These goals included things like killing an opponent, capturing a flag, recapturing their own flag, avoiding being killed, and so on. The programmers architected in that such rewards were *possible*, but let the learning algorithm define what those rewards actually were and how much the reward was for each one.
They call this architecture 'FTW'. Then they ran their vanilla "self-play from nothing" bots again, and found that just like in StarCraft, the bots never made much progress; but they found that the new bots, which had self-made internal rewards, were able to consistently beat strong humans, even after having their reaction time and visual accuracy reduced below that of measured humans.