Starcraft AI blog

Here’s the cumulative win rate graph for the bots that looked like they might be learning. I count UAlbertaBot as not learning, since the author said so.

The gyrations on the far left are mostly statistical noise. AIUR learns well, as we know. Tscmoo and Overkill also improve noticeably, each gaining about 3% in win rate between round 20 and the end (enough to move up 1 place in the tournament). LetaBot has a slight upward trend. The others look flat or even trend downward; either they are mislearning, or they are losing more games to the smarter learning bots, or they are drifting due to statistical noise. Statistical noise is usually bigger than your intuition says.

Among the learning bots, the three bots which learned best also finished best.

The non-learning bots:

Most look flat; all trends are slight, except that XIMP gains over 2% from round 20 to the end. Are the weak learning bots mislearning against it? It would be interesting to compare the non-learning bots that better withstood the increasing pressure of the successful learners to see if some common factor in their play made them harder to exploit, but that would be a tough analysis.

Bottom line: Tscmoo and Overkill each learned enough to overtake their nearest opponents, which was possible only because their nearest opponents were so near. AIUR increased its win rate by a giant 10% and overtook a few opponents early, but after round 25 no opponents were in reach. No other bot improved enough to make a difference. Learning, as implemented so far, can give a small edge to a few bots that do it well enough.

With more smarts, bots can learn more and faster. I’ll be suggesting ideas later on (I don’t run Machine Learning in Games for nothing). I hope to see bolder learning curves in this year’s competitions!