fading weights

Jump to navigation Jump to search

fading weights

Edited by author.
Last edit: 17:44, 28 June 2012

You do not have permission to edit this page, for the following reasons:

  • The action you have requested is limited to users in the group: Users.
  • You must confirm your email address before editing pages. Please set and validate your email address through your user preferences.

You can view and copy the source of this page.

 

Return to Thread:Talk:Diamond/Version History/fading weights.

Neat concept! Now this has got me thinking about trying to associate KNN weightings with all sorts of things besides time... distance maybe?

Rednaxela17:27, 28 June 2012
 

What do you use for your test bed? RoboResearch?

I've been wanting to systematize my testing more so I can shake things out more thoroughly in an automated way before I throw it up on the Rumble server. I've got RoboResearch ready to go.. I just need to assemble a set of bots to test against.

Tkiesel18:15, 28 June 2012

Since he's using genetic algorithms to optimize it, I assume he's using his WaveSim rather than RoboResearch for speed reasons. Trying to use RoboResearch with genetic algorithms would more or less be insanity I think ;)

Rednaxela18:24, 28 June 2012
 

Yeah, I use RoboResearch for real testing. For some gun-only testing (like this) I use WaveSim, which is a tool I wrote to test just classification against raw battle data - so it doesn't work for testing bullet power or some other nitty gritty things, but for the things it does work for, it's pretty sweet (and fast).

For test beds, I use User:Voidious/BedMaker, which is a little script I wrote to select random bots from the rumble within certain parameters. But you'd need to get a rumble server API key from Darkcanuck first. I've been thinking maybe I should make a web-based version of that for others to use freely, but I wasn't sure if anyone was interested...

Voidious18:24, 28 June 2012
 

Currently hunting down which of a bunch of changes between 1.7.29 and 1.7.35 caused a decrease in performance. I swear I tested rolling back each one individually yesterday and none of them fixed my score. So now the other way: start with 1.7.29 and add each change individually. Once that's done I can see if this fading KNN stuff actually helps.

I've been pretty fearless with continuous refactoring and bug fixes throughout 1.7.x, and this is the price I pay for it. =) But overall I think it's been worth it, both in terms of code quality and performance.

Voidious17:58, 29 June 2012
 

Orrrrr I've been chasing ghosts... In my 2,000 battle benchmark (250 bots x 8 seasons), 1.7.29 came in 0.18 above 1.7.37 and a dev version of 1.7.35 got the exact same score as 1.7.37. Rolling back individual changes gave me anywhere from 0.14 to 0.22 below 1.7.29's score. Then I recompiled 1.7.29, checked the class files came out the same (ie, I had the right source), and reran 10 seasons... and came in 0.14 below 1.7.29.

Another good reason to focus on big improvements: anything else is too small / painful to reliably benchmark. =)

Voidious19:25, 29 June 2012