Difference between revisions of "Talk:Gaff/Targeting"
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: A common problem with NN is overfitting -- you want to get as good an approximator as possible without losing the ability to generalize. By training waves only to within a certain tolerance, Gaff tries not to overfit. The tolerance margin is pretty small though: +/- 1/4 effective bot width at that distance. A while ago I did some tests and it seemed to help having it set fairly tight. Also note that the in-tolerance waves are rechecked every training interval (if still in the buffer), so if the net starts to forget them they do get re-trained. | : A common problem with NN is overfitting -- you want to get as good an approximator as possible without losing the ability to generalize. By training waves only to within a certain tolerance, Gaff tries not to overfit. The tolerance margin is pretty small though: +/- 1/4 effective bot width at that distance. A while ago I did some tests and it seemed to help having it set fairly tight. Also note that the in-tolerance waves are rechecked every training interval (if still in the buffer), so if the net starts to forget them they do get re-trained. | ||
: I guess you're thinking of not updating a VCS buffer if it's already producing the right answer? It would be interesting to see how that worked, or if it just flattened the buffer peaks too much... --[[User:Darkcanuck|Darkcanuck]] 01:46, 17 July 2009 (UTC) | : I guess you're thinking of not updating a VCS buffer if it's already producing the right answer? It would be interesting to see how that worked, or if it just flattened the buffer peaks too much... --[[User:Darkcanuck|Darkcanuck]] 01:46, 17 July 2009 (UTC) | ||
| + | : I've tried the more extreme version of that in my Anti-Surfer gun, decrementing bins (Dookious does everything in all bins covered by bot width) when my bullet hits, to simulate the enemy adapting to that knowledge. At some point, I thought it was giving a slight benefit, but at this point I think it has little effect either way. Though it's not quite the same thing, especially if you have [[Virtual Guns]]. --[[User:Voidious|Voidious]] 02:04, 17 July 2009 (UTC) | ||
Revision as of 03:04, 17 July 2009
This is really cool, thanks for writing it up. Makes me want to tinker. =) I'm curious, does the "anti-surfer" network alone do even better against surfers? --Voidious 19:24, 16 July 2009 (UTC)
- It was long overdue. Yes, the AS network scored 82.93 on the surfer portion of the TC2K7 (15 seasons) but only got 80.45 against the others. I continue to tweak it every once in a while but have yet to break the 83 barrier... --Darkcanuck 20:09, 16 July 2009 (UTC)
Ooh.. very interesting things here... The sentance that most intrigues me right now is "Waves that already give the correct solution are not retrained.". I have a hunch that this could be rather important in how well Gaff hits surfers, and may be useful outside of the world of NN guns too --Rednaxela 00:05, 17 July 2009 (UTC)
- A common problem with NN is overfitting -- you want to get as good an approximator as possible without losing the ability to generalize. By training waves only to within a certain tolerance, Gaff tries not to overfit. The tolerance margin is pretty small though: +/- 1/4 effective bot width at that distance. A while ago I did some tests and it seemed to help having it set fairly tight. Also note that the in-tolerance waves are rechecked every training interval (if still in the buffer), so if the net starts to forget them they do get re-trained.
- I guess you're thinking of not updating a VCS buffer if it's already producing the right answer? It would be interesting to see how that worked, or if it just flattened the buffer peaks too much... --Darkcanuck 01:46, 17 July 2009 (UTC)
- I've tried the more extreme version of that in my Anti-Surfer gun, decrementing bins (Dookious does everything in all bins covered by bot width) when my bullet hits, to simulate the enemy adapting to that knowledge. At some point, I thought it was giving a slight benefit, but at this point I think it has little effect either way. Though it's not quite the same thing, especially if you have Virtual Guns. --Voidious 02:04, 17 July 2009 (UTC)
