>How do you tell the neural net when to dust or ward? My knowledge on what is feasible with AI is fairly limited, so maybe a proposition like this is a bit nai

>How do you tell the neural net when to dust or ward? My knowledge on what is feasible with AI is fairly limited, so maybe a proposition like this is a bit naive - but wouldn't a more effective solution be to use some sort of machine learning/neural net hybrid that also incorporates data from matches played high-skill teams? From what I'm reading, OpenAI trains the neural net entirely by playing games against itself, and only uses matches against humans as benchmarks - so it essentially only develops strategies of play that primarily work well against itself. In most cases those probably coincide with strategies that work against human teams, but it seems like a lot of information is going to go missing - which is probably why it's only been successful in certain subsets of the game. It reminds me of a story I read a while back about two children who grew up relatively isolated from the rest of society, but had access to musical instruments. Since they had no teacher and apparently no other training materials, they developed an entirely unique style of performance and composition. Obviously in the case of music "success" is entirely subjective so simply being different doesn't invalidate it. But the point of OpenAI is to eventually beat human players in all aspects of the game - and if it's not going to actually train against humans, then success is going to be at least partially coincidental. So I would definitely agree that OpenAI (as it stands right now) has some potentially insurmountable weaknesses.