摘要
Machine learning has been widely applied to deal with problems in complex environment such as RoboCup, which is assumed as the ideal platform for research on AI and robotic. In RoboCup simulation league, software agents play soccer games on an official soccer server over the network. When constructing these software agents, issues in area of agents learning techniques arise to satisfy the properties specified by agent theorists. This paper presented an overview of the agents learning used in the simulator teams. Many kinds of agents learning techniques were reported and compared. It also provided open questions for discussing and pointed out some possible answers to verify in near future.
Machine learning has been widely applied to deal with problems in complex environment such as RoboCup, which is assumed as the ideal platform for research on AI and robotic. In RoboCup simulation league, software agents play soccer games on an official soccer server over the network. When constructing these software agents, issues in area of agents learning techniques arise to satisfy the properties specified by agent theorists. This paper presented an overview of the agents learning used in the simulator teams. Many kinds of agents learning techniques were reported and compared. It also provided open questions for discussing and pointed out some possible answers to verify in near future.
