ctak/include/weights.c, branch mainAn implementation of Tak and a computer opponent in Chttps://git.l-3.space/ctak/atom?h=main2026-08-28T18:37:41Zmight as well enable size 62026-08-28T18:37:41Ztslil clingmantslil@posteo.de2023-01-21T18:30:45Zurn:sha1:0e81096d5ecb6027814e7aae10b461e774f96407
Same network architecture, same training principle. Predictably this is
too slow.
Also statically allocate state in driver programmes.
switch to explicit game state & important bug fix & clang format2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2023-01-15T20:31:00Zurn:sha1:0223a9bec5535fced1a7698b55fd42155d9b0446
Previously the code base assumed that there was a single, global game
state which was the implicit target of all actions taken. Looking
ahead at architectural improvements, this has now been (almost
entirely) made explicit and functions take tak_state_p where
necessary (and also where unnecessary).
Two important fixes to actions.c were made:
- Previously when generating the possible stack moves, stack height
overflows (> 15) were not taken into account and this resulted in the
tree search corrupting the board state. Now action search does not
list all legal actions, rather the subset of these encodeable by the
implementation.
- The check for crushing on a stack move was incorrect (too strict),
and this resulted in many legitimate moves being igonored.
Finally, in other changes, weights have also been improved by training
all games instead of some subset for chosen players, and clang-format
was run on the codebase.
new neural network arch (faster + better) & minor changes + fixes2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2023-01-15T15:03:37Zurn:sha1:ee216c008a188a9436fedb85c70ee5d1719733b1
Gone is the convolutional neural network, for it turns out not only is
it more difficult to train, but all of the extra information about
board layers didn't make much of a difference at this size.
So cnn1986 has been replaced by nn1986, a standard, two-layer, dense
nn configured as a binary classifier and (mis)used in that capacity.
Note: total number of parameters is unchanged.
HARK: this new nn exposes a bug somewhere in ctak. Run ctlm with
self-play to see the completely borked board state at the end.
Fix copyright notice in files, and small preemptive optimisation2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2021-03-28T04:19:05Zurn:sha1:2dbb9fa9b69e49d49cebb1c17559f6fe67600a4d
Eventually there'll be a more complicated data generation step than
the one we're presently using, so having it in-lined in the loop is
wasteful. Ideally also this would be update per ply and we could avoid
recalculating it entirely for every query -- though it's probably
``fast enough'' for now. Also, caching is WIP.
Rename ct_k -> ct, IANAL but ...2026-08-28T18:37:41Ztsliltslil@posteo.de2021-02-05T21:49:14Zurn:sha1:ce09b6d94ac3dbf426e959b14fd7731001a003d1Just some #weightgoals ;)2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2021-02-01T04:33:39Zurn: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.
Added license information!2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2021-01-29T02:30:30Zurn:sha1:a77062ce3dc9a99ad0662010cc7eeb00dd5036a6Finally fixed memory order of weights!2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2021-01-18T16:44:50Zurn:sha1:c1750c0cb93a7b4ba70e1342eaa7c62e659f1dc5New weights from a wider training2026-08-28T18:37:41Ztslil clingmantslil@posteo.de2021-01-17T06:19:00Zurn:sha1:571afc6c72709910636979e20c0dd82dd0c66a46Went back up to 1986 weights2026-08-28T18:37:41Ztsliltslil@posteo.de2021-01-16T19:08:46Zurn:sha1:50b9955b040005e489127a11cf9c1648438a6266