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Robotic soccer requires the ability of individually acting agents to cooperate. The simulation league of RoboCup therefore offers an ideal testbed for evaluating multi-agent methods. In this paper we discuss how Reinforcement Learning (RL) methods can be succesfully applied within the scenario of learning to cooperatively score a goal. Due to the complexity of the task, enhanced methods of learning have to be applied. We discuss several approaches from literature and also present an own approach. All approaches are evaluated on a discretized version of robotic soccer, which we call gridworld soccer.

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Effective Methods for Reinforcement Learning in Large Multi-Agent Domains (Leistungsfähige Verfahren für das Reinforcement Lernen in komplexen Multi-Agenten-Umgebungen)