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Abstract

The present disclosure discloses a chess self-learning method and device based on machine learning, a move selection output layer and a value evaluation output layer of the method share the same input layer and hidden layer of a neural network, and a Monte Carlo tree search tree is used to construct a strategy optimizer. The training process of the method is divided into two parts, namely data generation and neural network training, so that an error between a value scalar outputted by a neural network and a final result of self-play is as small as possible, and a move vector outputted by the neural network is as close as possible to a decision vector given by a Monte Carlo tree for each search step. The present disclosure aims to construct an Al chess player for people to play chess.

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