Seeing comments here saying “this problem is already solved”, “he is just bad at this” etc. feels bad. He has given a long time to this problem by now. He is trying to solve this to advance the field. And needless to say, he is a legend in computer engineering or w/e you call it.
It should be required to point to the “solution” and maybe how it works to say “he just sucks” or “this was solved before”.
IMO the problem with current models is that they don’t learn categorically like: lions are animals, animals are alive. goats are animals, goats are alive too. So if lions have some property like breathing and goats also have it, it is likely that other similar things have the same property.
Or when playing a game, a human can come up with a strategy like: I’ll level this ability and lean on it for starting, then I’ll level this other ability that takes more time to ramp up while using the first one, then change to this play style after I have the new ability ready. This might be formulated completely based on theoretical ideas about the game, and modified as the player gets more experience.
With current AI models as far as I can understand, it will see the whole game as an optimization problem and try to find something at random that makes it win more. This is not as scalable as combining theory and experience in the way that humans do. For example a human is innately capable of understanding there is a concept of early game, and the gains made in early game can compound and generate a large lead. This is pattern matching as well but it is on a higher level .
Theory makes learning more scalable compared to just trying everything and seeing what works