Virtual Guns with Swarm Targeting?

I think the best would be having individual guns, and using leave-one-out cross validation to test which gun actually gives better results on the historical data at fire time. This would skip needing to track separate hitrates for guns that you don't know where they would fire.

If I understand correctly, for n firing waves w = {w1..wn}, LOOCV involves training the gun with w - {wi}, then test with wi, for all i. For knn guns, this means doing n “(k+1)nn” searches (then exclude wi from result), and for neural networks, this means feeding the network n(n-1) waves in total. Isn’t that costly for robocode? Or may there be some faster ways?

For KNN I think this is fine. I know personally I use a very fast kernel density function (square), so these kind of tests aren't expensive. That said, I haven't tried this technique yet. It might not work well due to other things we haven't spoken of yet.

Agreed though for NN, it is already pushing the limit of what can be processed in a single tick, so there is no way you can take 2x or 3x the processing power.

So do you mean only 2~3 waves need to be tested? I thought we need to iterate through every fired waves and do a knn search for each.

Medina currently implements the third approach, but it only uses a HOT and a PIF gun, which implies in no improvement, but is meant to be a place to work on in the future. It uses different ratings depending on the number of enemies alive.

That’s inspiring. Btw, how do you keep track of hitrates for individual enemies? Are you assuming it were in 1v1 when testing hitrate?

And how do you combine hitrate for individual enemy and estimate swarm hitrate?

I don't believe that any common targeting can outperform PIF in melee, and that's why virtual guns won't improve your gun. Of course there are special cases, e.g. I think Portia has a feature that it treats disabled bots differently, and that might be a useful improvement compared to mere PIF.

Well, I see many bots treating disabled bots differently, and that’s not hard to be implemented with swarm targeting.

While PIF guns play a dominant role in melee, similar to 1v1, there are two types of PIF guns, one tuned to hit the majarity, the other tuned to hit adaptive movement.

Although there are not so many adpative bots in melee comparing to 1v1, they still contribute more score diff per bot than those you can easily hit. So if we were able to hit adaptive movement better, in melee, the score improvements should be noticeable.

Also melee adaptive movement is growing in popularity — virtual guns with swarm targeting will one day be an essential part of state-the-art bot.

Combat uses oldskool GF with swarm targeting in melee.

Uses a disabled dimension to account for disabled bots. And uses data decay to account for adaptive movement.

Well that seems a good alternative to VGs in melee

I am thinking about implementation of hit rate time derivative metric against adaptive bots. What I see that at the beginning the circular gun is quite good against plenty of good and even top bots. But then it contaminates hit stats, i.e. adaptive bots are not hit by the circular gun anymore but other guns still have low but steadily improving hit rates.

Hit rates can be compared only among the last N tics, which would somewhat mitigates this. But it has no predictive power to see the gun which would be the best in the next round.

Interesting found! Maybe we could use knn to predict hit-rate in melee, just as bullet power is predicted in 1v1.

Take time, bots left and more into account, then maybe you can predict your hit-rate pretty well, and choose guns wisely.