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Each agent repeats a generic strategy over and over and does very little scouting/map vision. It doesn't scout and react very much at all. Most of its reactions are purely tactical (how it moves and controls its units)
Which is insanely difficult to learn which is cool - but it doesn't seem to have figured out the value of scouting and adapting. It plays solid by using generically good unit comps rather than adapting/reacting
Ahhh okay. That makes sense. I wonder how much of that is due to how it was told to "win". As I guess if you think about it, there are two different ways to optimize for winning: having a generic strategy you apply against everything or having strategies for specific situations
Part of me wonders if the ML decided somewhere along the line that the former was better or at least more or less the same as the later. I'm also a bit curious how much of this is a limitation of what it can "recall" as they talked about how it can end up chasing its own tail
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