ctak, branch sixes An implementation of Tak and a computer opponent in C https://git.l-3.space/ctak/atom?h=sixes 2026-08-28T18:37:41Z Playing around with huge nn's, poorly trained. 2026-08-28T18:37:41Z tslil tslil@posteo.de 2021-03-28T03:59:33Z urn:sha1:35dc86f8476250f693aa419f404b2b4c8c381d92 Not great at shallow depths Don't generate header for training data + tweaks 2026-08-28T18:37:41Z tslil tslil@posteo.de 2021-02-12T02:04:42Z urn:sha1:00a04c2929bdc8f8f4bf7d5d8cf413ebfb3cd006 For some reason it would seem that moving flats to a lower value and increasing the proximity between caps and top flats improves acquisition. Still not great, but every bit counts. Change the training data generation a little 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-11T01:07:24Z urn:sha1:890eb8d7f4a8c46eae18283ca5ac9c61fd41ed97 Although it pains me to say it, ``label smoothing'' appears to be actually work. I'm also currently experimenting with training simply against _all_ games, instead of only bot matches. Once the training finishes i'll pit cttei against itself with old and new weights, hopefully there'll be a noticeable improvement. Rename ct_k -> ct, IANAL but ... 2026-08-28T18:37:41Z tslil tslil@posteo.de 2021-02-05T21:49:14Z urn:sha1:ce09b6d94ac3dbf426e959b14fd7731001a003d1 Small typo in generated output for weights 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-05T21:37:32Z urn:sha1:5bc1e056412cb7d96831ab7ae32842c166b38436 Correct line clearing behaviour, EXIT_FAILURE <~ -1 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-05T21:24:39Z urn:sha1:fb99bf2cf28fe236ef06c5df30d6b8cbfad9897e Renamed binaries 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-05T19:02:36Z urn:sha1:5c475a552dbf16589d212bc7de7ed526b47aad08 TEI interface working! 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-03T00:23:54Z urn:sha1:40c6b1dafab4de169bac8a799e7953e85061218e Tried some naive iterative deepening. Work on TEI interface next 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-02T02:46:24Z urn:sha1:11956b2e940f5e1839efab187d898092e819a766 If TEI is implemented, then i could make use of Morten's racetrack (https://github.com/MortenLohne/racetrack) and develop a quantitative measure of the bot's performance. This is the current priority. Just some #weightgoals ;) 2026-08-28T18:37:41Z tslil clingman tslil@posteo.de 2021-02-01T04:33:39Z urn:sha1:328c8d1e3094a942d6a2edd933c9cc4ab09daab1 It turns out that while i was training on a 0/1 classification problem, i was using 2*eval - 1. Training using this function instead, and on bot-dominated game choices (chosen_player in extract.sh) seems to have given a better evaluation function. At the least, Morten's swindle doesn't work anymore.