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FastChess Screen [1]

a didactic open source chess engine by Thomas Dybdahl Ahle, written in Python, licensed under the GPL v3.0. FastChess predicts the next move by probing a one-layer neural network softmax model, using the fastText text classification library. The model takes the board state as input, and outputs a vector of probabilities for each possible move. That simple linear model might further be combined with a Monte-Carlo tree search along with the PUCT selection to improve the quality of play [2].


FastChess' model is trained by feeeding a set of pgn files to a special training procedure, creating the neural network weights in form of a model.bin file, which is later used to play chess [3].


FastChess' hyperparameters can be tuned with black box optimization through scikit optimize [4].

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