An implementation of Tak and a computer opponent in C https://git.l-3.space/ctak/atom/include/negamax.h?h=hybrid_rootstrap 2026-09-16T20:02:53Z 2026-09-16T20:02:53Z tslil tslil@posteo.de 2026-09-16T19:10:33Z urn:sha1:1898add0142b70b3fef5af78a6f3d7399a3f2cd5 A little subtle, but in non-self-play games sharing tt entries is incorrect. We must therefore key additionally on the evaulation function itself. 2026-09-04T20:03:59Z tslil tslil@posteo.de 2026-09-03T19:17:22Z urn:sha1:1ca3309dad9d412870b2b793f33af7a7ccabd514 TD-lambda and TD-leaf are separated by the observation that minimax with a parameterised leaf heuristic is differentiable* in the parameters. "All" that had to be done was to extract the "principal variation": which one position survived all of the pruning to become the reported value. This was a bit fiddly and uncovered a subtle bug (again) in negamax.c: we were not being complete in our overriding of the TT entry and by omitting key it was possible that later searches would find incorrect values. The more interesting question to me was: how do we introduce enough variety while not corrupting the PV chain? I settled on having selfplay have one of the players be epsilon-random but the other greedy (minimax) and simply report only the greedy portion PV chain. This appears to work well enough in practice and doesn't introduce nonsense training data for the net (we don't force its actions to be nonsensical). I also deciced, perhaps in a departure from tradition, that we had good supervised labels for PVs that happened to be winning states, so the training overrides the targets in that case. Finally rolled back some of the ambition in nn_train.py default arguments. *almost always 2026-09-02T11:13:01Z tslil tslil@posteo.de 2026-08-30T18:46:07Z urn:sha1:241156da6f35cb888253e6b8814e7b61a76b4fc7 NOTE: 6x6 weights are as-yet untrained On the performance side: * switch from linked-list chaining hash table to direct mapping * cache check_win by hash * persist transposition tables across plies (this was a big deal and difficult to puzzle out) * add align directives to the network floats * prepare_nn_input gained a small LUT to avoid branching (i suppose you might call this 1D embedding) * apply mtune=native for the native builds On the correctness side: * Zobrist has now includes the ply parity * the network now predicts something in [-1, 1] using tanh (approximated by polynomials) * the training object is now MSE because we're learning a value estimate * use .clangd instead of compile_commands.json On the training side: * completely forgo human-derived data set * implement td(lambda), self-play, tournaments 2023-01-21T21:00:57Z tslil clingman tslil@posteo.de 2023-01-21T18:30:45Z urn:sha1:72c5056c5aa614d9a86f8b760955cfcf96c44be4 Same network architecture, same training principle. Predictably this is too slow. Also statically allocate state in driver programmes. 2023-01-16T11:25:47Z tslil clingman tslil@posteo.de 2023-01-15T20:31:00Z urn:sha1:ef785da721a47e6d13aa854c795d2a9e269285b0 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. 2023-01-15T15:03:37Z tslil clingman tslil@posteo.de 2023-01-15T15:03:37Z urn:sha1:81213c273fd874aba95c45bd692e14079ebce434 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. 2021-10-05T22:23:32Z tslil clingman tslil@posteo.de 2021-10-05T22:22:23Z urn:sha1:c9fa20b590de94a9ddb408e277065a117b16570b This is a special interface to negamax_cnn1986 which is designed to generate output for use in a CGI tak interface to be used over gemini. Also in this commit is a reformating of the various source files to use the traditional tab width of 8 spaces. 2021-03-28T04:19:05Z tslil clingman tslil@posteo.de 2021-03-28T04:19:05Z urn:sha1:b8a88e53aa2a933d39777ba435a6cad9f5c42428 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. 2021-02-05T21:49:14Z tslil tslil@posteo.de 2021-02-05T21:49:14Z urn:sha1:5c8e082276a24b9f266396874d3e72c05990a3df 2021-02-02T02:46:24Z tslil clingman tslil@posteo.de 2021-02-02T02:46:24Z urn:sha1:41b5dc810e25724e8111cf7f2ad1b4b52f9b1206 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.