Reinforcement learning for training a computer program of Chinese chess

Abstract

Computer chess has always been an interesting subject in artificial intelligence (AI). We propose a method to design a Chinese chess program that improves its performance through training. In this study, we utilise temporal-difference learning, which is a method of reinforcement learning, where each position receives reward value from the next position and the value of the position is modified by a heuristic evaluation function. The experimental results show that the program indeed improved its performance by reinforcement learning.