Starcraft AI blog

One of the earliest improvements I made for the upcoming Steamhammer 2.0 for AIIDE was superior zergling micro. In an equal fight, zerglings on zerglings, Steamhammer tended to come out ahead of Killerbot more often than behind. Now, after many other changes, I have somehow broken it, and Steamhammer loses equal fights. I tried undoing all the changes that seem as though they could be related, but no go. I don’t know what I have done wrong. It’s amazing how fragile some skills are.

Weaknesses are stubborn. The first wipe doesn’t clear away the dirt, you have to scrub repeatedly. I made changes to improve kiting, and hydralisk play is noticeably sharper. But mutalisk micro is worse, because the changes caused seemingly unrelated code to misbehave. I think it will be easy to fix, but what weaknesses did I introduce without noticing?

In another example. I have been systematically working through Steamhammer’s openings to fix faults that have crept in because of other changes: Queue reordering, queue jam clearing rules, mineral locking, and so on. Most openings are OK, but a surprising number need minor adjustments. I also found a subtle one-frame-late timing bug in the interaction between the building manager and the strategy boss that broke a single rarely-played opening. Everything is connected.

Strong interdependence seems to characterize Starcraft skills. When you change one point, it doesn’t matter if it’s a big improvement, you have to also align other points to realize the improvement. As Dan Gant expressed it, a bot is usually at a local maximum in skill. When you nudge it in any direction, it is no longer at a maximum, and you have to hunt around for a new local peak, which you hope is higher.

It makes progress bumpy. On the one hand, that drives me to machine learning: I don’t want to get all these details right by hand! It’s too much! On the other hand, it poses tough problems for machine learning algorithms, too, whether learning holistically or piecemeal. Such a complex and difficult fitness landscape is hard to optimize over, whether by hand or by machine.