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| author | tslil clingman <tslil@posteo.de> | 2023-01-15 21:31:00 +0100 |
|---|---|---|
| committer | tslil <tslil@posteo.de> | 2026-08-28 19:37:41 +0100 |
| commit | 0223a9bec5535fced1a7698b55fd42155d9b0446 (patch) | |
| tree | e7a980454e65d88b56194eed733cabec29ef51b5 /README.md | |
| parent | ee216c008a188a9436fedb85c70ee5d1719733b1 (diff) | |
switch to explicit game state & important bug fix & clang format
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.
Diffstat (limited to 'README.md')
| -rw-r--r-- | README.md | 2 |
1 files changed, 1 insertions, 1 deletions
@@ -42,7 +42,7 @@ Total params: 1,986 Trainable params: 1,986 Non-trainable params: 0 ``` -which was trained on the binary classification problem of predicting the winner from a given board state. As input the network is fed the top layer of the board only (see nn1986.c for details) as well as `flats used/flats remaining` fractions for both players and a single float indicating the parity of the board. At the time of training, on the dataset given by `resources/extract.sh`, this achieves ~82% accuracy on the validation set. +which was trained on the binary classification problem of predicting the winner from a given board state. As input the network is fed the top layer of the board only (see nn1986.c for details) as well as `flats used/flats remaining` fractions for both players and a single float indicating the parity of the board. At the time of training, on the dataset given by `resources/extract.sh`, this achieves ~75% accuracy on the validation set. ## License |
