Heterogeneous multi-task learning of evaluation functions for chess and shogi

Abstract

Using advanced deep learning methods, artificial intelligence is able to achieve unprecedented high performance in playing complex board games. However, in conventional practice, models for different games require separate training with domain-specific datasets, which is not conducive to enable the full use of the correlation between tasks and may cause unnecessary consumption of computing resources. This paper presents a novel multi-task learning framework for the training of deep-convolutional-neural-network-based evaluation functions for two heterogeneous but related games – chess and shogi. Experimental results show that the application of the proposed framework improved the prediction accuracy for both networks with limited training steps.