Comparison Training of Shogi Evaluation Functions with Self-Generated Training Positions and Moves
説明
Automated tuning of parameters in computer game playing is an important technique for building strong computer programs. Comparison training is a supervised learning method for tuning the parameters of an evaluation function. It has proven to be effective in the game of Chess and Shogi. The training method requires a large number of training positions and moves extracted from game records of human experts; however, the number of such game records is limited. In this paper, we propose a practical approach to create additional training data for comparison training by using the program itself. We investigate three methods for generating additional positions and moves. Then we evaluate them using a Shogi program. Experimental results show that the self-generated training data can improve the playing strength of the program.