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The existing reinforcement learning methods have been seriously suffering from the curse of dimension problem especially when they are applied to multiagent dynamic environments. One of the typical examples is a case of RoboCup competitions since other agents and their behavior easily cause state and action space explosion. This paper presents a method of modular learning in a multiagent environment by which the learning agent can acquire cooperative behavior with its teammates and competitivedoi:10.1163/156855308x344882 fatcat:vt3s2tlgwbesxahc7rdic4p3pe