Dodging Performance Anomaly?

I have recently discovered robocode, and I made a relatively simple bot using DookiCape movement and a simple but somewhat unique segmented GF gun(distance, lateralV, advancingV). I don't know if you are still interested in improving your robot, but I noticed that after a while in a 1000 battle of my bot vs Pris my hit rate went much higher than it should have, all the way up to 20%. I don't know why my targeting is working so well, I don't even decay old data right now. You may want to look into Pris' surfing for bugs etc. P.S. (My bot won the 1000 round battle with a 78% score share. By comparison, my bot scores 35% vs Shadow in a 1000 round battle)

Unfortunately, I don't think Darkcanuck comes around here any more. That is interesting though. I wonder if something about the neural net gets corrupted? I remember that TheBrainPi, which saves its neural net to disk between matches, had a bug that was solved by deleting its neural net data file (so it could start fresh, I guess).

It's also worth noting that RoboRumble matches are 35 rounds, so that's what many of us use in most or all of our testing. I bet a lot of top bots have issues in 1000 round battles.

And welcome to the wiki. ;)

Thanks for the amazingly fast reply. And for the movement system, I've only been working on robocode for about a month, and I started on targeting first. Another interesting thing is that DrussGT only scores 73% with a 17% hitrate against Pris, worse than mine, yet it totally trounces my bot in a direct battle. Has anybody thought about using Random Forest for movement or targeting? It uses an ensemble of decision trees for classification. Its slow to generate a forest, but running data through one is pretty fast. I could imagine a robot which only retrained a few of the trees in a forest every tick. Seems somewhat similar to what people are doing with K nearest neighbor classification.

I've looked at random forests before, another one which interested me was Extreme Learning Machines which are feed-forward NNs working in an ensemble. The trouble I found was that even though these methods are fast when compared to other machine learning techniques (K-means, feedback NN), they are still much slower than a single KNN call in a Kd-Tree just because of the amount of data they need to touch for each 'event'. A Kd-Tree trains in O(1) and classifies in O(logN).

Feel free to prove me wrong though =) I'd love something which works well beyond the ubiquitous Kd-Tree!

Another thing to consider is how you are going to pose the question. A lot of the successful NN-based approaches have used a bunch of classifiers, one for each potential firing angle, and shooting at the one with the highest probability. Others have tried posing it as a straight regression problem, but I don't think those worked as well, possibly because of the high noise (against top bots you are lucky to get a 10% hitrate).

I'd be interested to hear what you end up trying, and how it works out.

Even in a 35 round battle, my robot still barely wins at 51%, which is far higher than its score against comparable bots in the rumble. This is also with no preloaded GF data, it blindly fires HOT at first.