3 ms·
By "training rules" are you referring to the way machine learning systems are traiend? In that case, you're right. The Challenge highlights an important limita
by YeGoblynQueenne 4y ago
By "training rules" are you referring to the way machine learning systems are traiend? In that case, you're right.
The Challenge highlights an important limitation of modern machine learning systems, that they can only learn from their training data and have only a very, very limited ability to incorporate "background knowledge".
"Background knowledge" in this case is the "spoiling" you're refering to. There is virtually no way to teach a neural net system how to play Nethack, other than to let it try, and fail, and fail again, at ascending. You can't explain to it for example, how writing "ELBERETH" in the dirt with your finger will scare monsters away (which is important background knowledge about the game that is very hard to learn just by trial and error).
- ajsnigrutin 4y agoNethack is full of such things, that would be meaningless in many other games, and AI developers would probably not look at in other games... eg. what your pet stops on (or doesn't step on/pick up) and its curse status. If you don't track the dog, remember the status, and then notice the un/cursed status in the end, you never find out stuff like that.
- YeGoblynQueenne 4y agoYes. Some of those things can be discovered by trial-and-error (exploration in Reinforcement Learning, I guess?) but some others are much more of a tall order. One classic problem is that the things one does in early levels affect the progress of the game at higher (lower) levels but it's not clear how before getting there. So while I think that, ultimately, a big neural net should be able to learn all those tricks that human players have learned over the years, it would take an exhorbitant amount of data and training time, that perhaps few companies would want to spend on something obscure and esoteric like nethack.