3 ms·

Self play applies to adversarial games, where both players are the same AI being trained (instead of AI VS humain or AI vs best known AI). Game of life is not

Self play applies to adversarial games, where both players are the same AI being trained (instead of AI VS humain or AI vs best known AI). Game of life is not an adversarial game, there is no opponent, you can not apply self play to learn the rules.

rollcat 6y ago
So... I'm no AI expert, but I play some StarCraft 2, and I'm curious about how exactly Alpha Zero learns and why it's not applicable to some problems. Contrary to go or chess, StarCraft includes asymmetric matchups (three very, very distinct races, so in 1v1 - six possible matchups). Every matchup is played very differently; one trivial example: if you'd try to play a Protoss vs Zerg match like you'd play a Protoss vs Protoss, you're going to lose really, really badly within minutes: the standard PvP opener (wall-off at the main base) is going to leave your natural expansion completely defenceless against a trivial early attack (mass zerglings), which snowballs into a very, very bad economical disadvantage. So basically when AlphaStar learns, it absolutely must be really good at handling these kinds of asymmetric scenarios (both players/actors having a vastly different arsenal at their disposal, starting from t=0), otherwise it would lose every single non-mirror matchup (while it's actually beating many grandmasters). Another peculiarity of StarCraft is e.g. with Zerg, as the race that normally opts in to play the very early game a bit blindly (late scout with a slow Overlord vs an earlier worker scout), but must still make some strategic choices, the most common (and optimal) being greed, which occasionally means losing to aggression or missing a chance to punish a greedy opponent. Humans here kind of rely on intuition, metagame, etc. My question is, can the capabilities to handle the asymmetry, and to play blindly, be exploited by re-modeling a class of problems, such that they become "StarCraft-like" enough? E.g. (naively) if the Game of Life board is not "correct enough" by iteration N, the opponent will flood your base with zerglings ;)

rollcat 6y ago

So... I'm no AI expert, but I play some StarCraft 2, and I'm curious about how exactly Alpha Zero learns and why it's not applicable to some problems. Contrary to go or chess, StarCraft includes asymmetric matchups (three very, very distinct races, so in 1v1 - six possible matchups). Every matchup is played very differently; one trivial example: if you'd try to play a Protoss vs Zerg match like you'd play a Protoss vs Protoss, you're going to lose really, really badly within minutes: the standard PvP opener (wall-off at the main base) is going to leave your natural expansion completely defenceless against a trivial early attack (mass zerglings), which snowballs into a very, very bad economical disadvantage. So basically when AlphaStar learns, it absolutely must be really good at handling these kinds of asymmetric scenarios (both players/actors having a vastly different arsenal at their disposal, starting from t=0), otherwise it would lose every single non-mirror matchup (while it's actually beating many grandmasters). Another peculiarity of StarCraft is e.g. with Zerg, as the race that normally opts in to play the very early game a bit blindly (late scout with a slow Overlord vs an earlier worker scout), but must still make some strategic choices, the most common (and optimal) being greed, which occasionally means losing to aggression or missing a chance to punish a greedy opponent. Humans here kind of rely on intuition, metagame, etc. My question is, can the capabilities to handle the asymmetry, and to play blindly, be exploited by re-modeling a class of problems, such that they become "StarCraft-like" enough? E.g. (naively) if the Game of Life board is not "correct enough" by iteration N, the opponent will flood your base with zerglings ;)