Starcraft AI blog

No big analysis, but I do want to point out a couple features of the cumulative results over time.

#1 Stardust, #3 PurpleWave, and #8 UAlbertaBot largely declined over the tournament, or declined and leveled out. They were outlearned by the competition. Steamhammer is slow to learn but keeps learning for a long time, and its curve is fairly flat.

The two bots with the most impressive learning curves are #7 Dragon, a carryover from last year, and #9 InfestedArtosis, the tail ender. Dragon improved between round 25 and 100. Infested Artosis improved after round 100. Between round 100 and the end of the tournament, InfestedArtosis increased its score by 3.1% in absolute terms, more than any other bot improved after round 100. No other bot comes close over the same period. The weakest bot outdid all others in continuing to play better over the entire long tournament.

InfestedArtosis has a simple learning method. It independently learns three things, each from a short menu of choices: What opening build to play, what unit mix to follow up with, and whether to build static defense. Imagine how well it might learn if it, say, approximated the joint distribution and chose based on that!