Dodging Performance Anomaly?
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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.