Great summary.
I'll add:
- The coordination between AI bots are clearly beyond human level. Or at least as demonstrated from similar performance from the humans on similar style of heros.
(It might not appear too different from the show match, but based on my 2-3k hours watching pro games, the coordinations are noticebaly better than the best team in history, aka Wings gaming 2016 TI champion).
I am not sure how such coordinations are modeled in dnn, which itself seems the most valuable from this research.
- In general I think with this show match, it pretty much sealed the doom of human players in dota2.
As it shows that the general approach is scalable and capable to handle the problem itself. As from laning to team fight, and item building, the AI did not show weakness at all.
I was worrying about AIs general inefficiency in deriving the winning strategy, laning stage, and team fight coordinations, which turns out to be obviously superior to human players.
Drafting probably will be even more favorable to AIs. The challenge would be can they train faster by observing the change log, I.e. finding winning strategy without training from scratch each time after a patch release.
I seem no reason AIs lose to vp/liquid/lgd (the top 3 going into ti8). The idea that split pushing hero can deal with the team fights seems underestimate the AIs discipline, which is clearly superior to the best humans.
- Last is how much computing resources are used in the training and playing. Hopefully value can team with open ai to release a benchmark bot team for calibration and a different ladder systrm of playing different AI strength level
I'll add:
- The coordination between AI bots are clearly beyond human level. Or at least as demonstrated from similar performance from the humans on similar style of heros.
(It might not appear too different from the show match, but based on my 2-3k hours watching pro games, the coordinations are noticebaly better than the best team in history, aka Wings gaming 2016 TI champion).
I am not sure how such coordinations are modeled in dnn, which itself seems the most valuable from this research.
- In general I think with this show match, it pretty much sealed the doom of human players in dota2.
As it shows that the general approach is scalable and capable to handle the problem itself. As from laning to team fight, and item building, the AI did not show weakness at all.
I was worrying about AIs general inefficiency in deriving the winning strategy, laning stage, and team fight coordinations, which turns out to be obviously superior to human players.
Drafting probably will be even more favorable to AIs. The challenge would be can they train faster by observing the change log, I.e. finding winning strategy without training from scratch each time after a patch release.
I seem no reason AIs lose to vp/liquid/lgd (the top 3 going into ti8). The idea that split pushing hero can deal with the team fights seems underestimate the AIs discipline, which is clearly superior to the best humans.
- Last is how much computing resources are used in the training and playing. Hopefully value can team with open ai to release a benchmark bot team for calibration and a different ladder systrm of playing different AI strength level