Combination of Q-learning and Fuzzy-State for Learning of RoboCup Agent

• Main Authors: Wei-De Su, 蘇偉德
• Other Authors: Kuo-Jong Yih
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/jg434k

碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 98 === Artificial intelligence always been interesting in computer science,and in this area machine learning is the key to success, RoboCup(Robot World Cup Tournament) is a competition game which has already become a popular research domain in recent years, includes the real robot as well as computer simulation games and also provide comprehensive rules and mechanisms. In Academic,it provides a best test-bed for machine learning. As the soccer game, the environment states are always changing.Therefor, in this paper, we use the Q-Learning method that is a kind of reinforcement learning to apply for learning of robocup agent. And, in order to solve the environment states of excessive problem which led to slow learning rate, we will use fuzzy-state and fuzzy-rule to decrease the state and state-action table of Q-Learning.