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Wow, was looking for this (old) challenge! Thank you, MultiplyByZer0.

Regarding the experiment, I know NN has a problem of slow learning since there isn't much data at the beginning of the game. Couldn't a reinforcement of firing waves in the first few rounds solve the problem, though? Another suggestion would be to use waves with low virtuality (those tick waves which are close to a firing tick) to suppress the lack of information without polluting the network with flattening-like data right on the first rounds.

I did something like that in my gun and it improved a lot. Of course, I'm no reference in Neural Targeting: I improved from a really bad gun to a miserable one :) Well, maybe you've already done that after all.

• • Thank you MultiplyByZer0. I found out which part of my movement was weak.

• • I will probably solve the fast targeting methods problem with initial predictors. It is hard to learn how CT/LT guns fire since they use velocity/heading/heading change which I don't use.

• • I don't think that Gun needs more data. There is already tons of data. I generally use a weighted crowd system which gives pretty good results both in TCAS and TCRM. I don't think about the gun too much right now.