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> the mechanics are certainly random in the sense that they involve dice rolls. Sorry, but that's still not what I've meant. Dice rolls are randomness in the

> the mechanics are certainly random in the sense that they involve dice rolls. Sorry, but that's still not what I've meant. Dice rolls are randomness in the usual meaning of this word. But "random" can also mean "arbitrary" and/or "illogical" things. Imho Nethack mechanics are also random in this sense. They don't make sense at all… :-) > Even moderately experienced players will fail close to 100% of the time. So getting to an almost 50% success rate, to my mind, shows a great deal of skill! OK, you have definitely a point here. Still not my cup of tea, such games. (And this is strictly personal, and unrelated to the rest of the discussion). I just don't like games where it's very likely that I will loose despite "doing everything right". I have no problem with games punishing merciless even small mistakes. That's OK. But having an outcome that depends mostly on the whim of the RNG is just nothing I enjoy. When I "do everything right" I like to get rewarded appropriately for it. (Of course some level of randomness is still OK. But if the RNG kills you most of the time no mater what you do this is just too frustrating for me. So I'm clearly not the target audience for Nethack… :-D). > I don't think this is true any more for modern AIs such as AlphaGo and its predecessors, which learn distributions of possible outcomes rather than deterministic predictions. Sure. But how do you learn from a distribution where no matter what you do you will fail in, say, 99,9% of the cases? > IIRC the latest versions can even self-play games like Poker to a superhuman level. Do you have some links regarding this? I thought Poker is still one of the games where AIs don't play better than humans. OK, maybe it depends on the Poker variant. There are simpler and more difficult ones. > If there is any systematic deviation from pure randomness, enough data will reveal it! I would agree in general. But now we're back to the initial question: Is there enough systematic deviation from pure randomness in Nethack? Given that even people who know all the mechanics, and know some good end-to-end strategies will fail in most cases (actually, like you said, in almost all cases). And given that it's (imho) impossible to come up with this knowledge about mechanics and strategy just by playing this game. I have my doubts.

bubblyworld 3y ago
> Do you have some links regarding this? I thought Poker is still one of the games where AIs don't play better than humans. OK, maybe it depends on the Poker variant. There are simpler and more difficult ones Texas Hold'em, one of the most popular variants - have a look at deep mind's Player Of Games, and the general technique of Counterfactual Regret Minimisation. Both are recent advances, but poker is absolutely solved at a human professional level now. I think one thing to keep in mind is that what you find illogical has very little bearing on whether a neural net can learn to do it. Us humans come prebaked with specific priors in our brain (like cognitive biases) that AIs don't necessarily share. I'd be careful making sweeping statements about what is impossible and what isn't, personally. But I guess we'll see =) > Sure. But how do you learn from a distribution where no matter what you do you will fail in, say, 99,9% of the cases? You can actually test this yourself if you're interested - try to train a neural net to predict outcomes that are deterministic but with a 99.9% chance of random failure. If the net learns to succeed 0.1% of the time then your premise is false - it has successfully extracted the signal!

bubblyworld 3y ago

> Do you have some links regarding this? I thought Poker is still one of the games where AIs don't play better than humans. OK, maybe it depends on the Poker variant. There are simpler and more difficult ones Texas Hold'em, one of the most popular variants - have a look at deep mind's Player Of Games, and the general technique of Counterfactual Regret Minimisation. Both are recent advances, but poker is absolutely solved at a human professional level now. I think one thing to keep in mind is that what you find illogical has very little bearing on whether a neural net can learn to do it. Us humans come prebaked with specific priors in our brain (like cognitive biases) that AIs don't necessarily share. I'd be careful making sweeping statements about what is impossible and what isn't, personally. But I guess we'll see =) > Sure. But how do you learn from a distribution where no matter what you do you will fail in, say, 99,9% of the cases? You can actually test this yourself if you're interested - try to train a neural net to predict outcomes that are deterministic but with a 99.9% chance of random failure. If the net learns to succeed 0.1% of the time then your premise is false - it has successfully extracted the signal!