Starcraft AI blog

Here is what AIUR learned about each opponent over the course of the tournament. I did this mostly because it’s easy; I already had the script from last year. But it’s also informative—AIUR’s reactions tell us how each bot played, and may tell bot authors what they need to work on.

The data is generated from files in AIUR’s final read directory. AIUR recorded 111 games against some opponents even though the tournament officially ran for 110 rounds; that is presumably because the tournament did run longer but was cut back to a multiple of 10 rounds for fairness (since there are 10 maps). On the other hand, AIUR’s total game count according to itself is 2938 and according to the tournament results is 2965, so it may have been unable to record some games (it is listed with 53 crashes, so that’s not a surprise). First an overall view, totalling the data for all opponents. We can see that all 6 of AIUR’s strategies (“moods” it calls them) were widely valuable: Every strategy has win rate over 50% on some map size. AIUR’s overall win rate in the tournament was 50.46%.

• 2, 3, 4 - map size, the number of starting positions
• n - games recorded
• wins - winning percentage over those games
• cheese - cannon rush
• rush - dark templar rush
• aggressive - fast 4 zealot drop
• fast expo - nexus first
• macro - aim for a strong middle game army
• defensive - be safe against rushes (not entirely successful)

AIUR struggled against the tournament leader but was not entirely helpless. Its cannon rush had a chance on 2 player maps and its anti-rush strategy on the others. We see how AIUR gains by taking the map size into account.

AIUR upset #2 PurpleWave, a surprising outcome. The DT rush and the fast expand were both somewhat successful—rather unrelated strategies.

Learning can’t help if nothing you try wins....

Cpac was configured to play 5 pool against AIUR. It worked, but AIUR was able to compensate to an extent by playing its anti-rush build.

Microwave was successful but showed a little vulnerability to surprise zealots dropped in its main. I suspect it’s a tactical reaction issue.

AIUR upset McRave with its cannon rush, and the dark templar rush did well too. AIUR executes the best cannon rush of any bot, in my opinion. It is a sign that McRave’s play was not robust enough against tricks.

The DT rush won 100% of the time on 2 player maps and was tried only a few times on larger maps, losing. Was it only unlucky on the 3 and 4 player maps, or is there a real difference? With only 3 games total, we can’t tell from the numbers. It is a weakness of AIUR’s learning: It’s slow because there is so much to learn. The flip side of the slowness is that, over a long tournament, it learns a lot.

I was surprised to see Steamhammer upset by AIUR. I had thought that AIUR was a solved problem. On SSCAIT too, Steamhammer started to show losses against AIUR in September for the first time in months. I may have introduced a weakness in some recent version and AIUR’s learning took that long to find it on SSCAIT. In AIIDE, the tournament was easily long enough.

I suspect that fast expo was the best strategy on 4 player maps, but how was AIUR to know? A weakness of AIUR’s epsilon-greedy learning, compared to UCB, is that it doesn’t realize that a less-explored option is more likely to be misevaluated.

What’s up with all those zeroes? AIUR is coded to try each strategy once before it starts making decisions, and that did not happen here. It turns out that AIUR has pre-learned data for Skynet, XIMP, and UAlbertaBot, so its learning in those cases looks different.

Against Xelnaga, AIUR found solutions on 2 and 3 player maps but not on 4 player maps. Is it another case of underexploration?

Juno’s cannon contain upset AIUR. Learning didn’t help much, because the problem wasn’t in any of the strategies, it was in AIUR’s poor reactions to cannons appearing in front of its base. It is amusing to watch 2 bots cannon each other when sometimes both get cannons up.

Here is another possible case of insufficient exploration. The 4 zealot drop won 100% of the time on 2 player maps and 96% of the time on 4 player maps, but was never tried on 3 player maps (I guess due to a crash, since AIUR tries to play each strategy once). It’s not a severe problem, though, because 3 player maps did have 2 strategies that scored 100%.

Next: AILien’s learning.