Abstract
In order to solve the state explosion problem of reinforcement learning in soccer game simulation, a state-space reduction method using fuzzy logic is proposed. We apply the proposed method that state-space will be reduced from several millions to several hundreds to soccer agent model of Robocup 2D soccer simulation system. Through the experimental comparisons between the proposed method and the conventional soccor agent without learning in 20 games, it is confirmed that the proposed method improves the performance of taking goal of soccer agent.
