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@@ -12,7 +12,7 @@ Details forthcoming, but at a glance: ## Building for the native platform - make native + make native ## Building for the Raspberry Pi Zero @@ -20,17 +20,29 @@ Download and extract a recent version of buildroot into the working directory. Edit `BUILDROOT_DIR=buildroot-2020.11.1` in `Makefile` to point to the extracted directory. - make pi + make pi ## The computer opponent -### Adversarial tree search implementation +Standard adversarial tree search (α-β negamax) with iterative deepening, using a neural network evaluation function for leaves and also transposition tables implemented using Zobrist hasing and a chaining hash table. -Details coming soon +The architecture of the neural network is a standard, two-layer dense classification network +``` +_________________________________________________________________ + Layer (type) Output Shape Param # +================================================================= + input_1 (InputLayer) [(None, 28)] 0 -### The convolutional neural network cnn1986 + dense (Dense) (None, 64) 1856 -Details coming soon + dense_1 (Dense) (None, 2) 130 + +================================================================= +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. ## License |