Towards Collaborative and Adversarial Learning: A Case Study in Robotic Soccer

Peter Stone and Manuela Veloso

Soccer is a rich domain for the study of multi-agent learning issues. Not only must the players learn to adapt to the behavior of different opponents, but they must learn to work together. We are using a robotic soccer system to study both adversariai and collaborative multi-agent learning issues. Here we briefly describe our experimental framework along with an initial learned behavior. We then discuss some of the issues that are arising as we extend our task to require collaborative and adversarial learning.

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