From 0223a9bec5535fced1a7698b55fd42155d9b0446 Mon Sep 17 00:00:00 2001 From: tslil clingman Date: Sun, 15 Jan 2023 21:31:00 +0100 Subject: 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. --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) (limited to 'README.md') diff --git a/README.md b/README.md index 9f18cad..8d1b437 100644 --- a/README.md +++ b/README.md @@ -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 -- cgit v1.2.3