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Towards collaborative and adversarial learning: a case study in robotic soccer

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Abstract

Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn lower-level skills, but they must also learn to work together and to adapt to the behaviors of different opponents. We are using a robotic soccer system to study these different types of multiagent learning: low-level skills, collaborative and adversarial. Here we describe in detail our experimental framework. We present a learned, robust, low-level behavior that is necessitated by the multiagent nature of the domain, viz. shooting a moving ball. We then discuss the issues that arise as we extend the learning scenario to require collaborative and adversarial learning.

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This research is sponsored by the Wright Laboratory, Aeronautical Systems Center, Air Force Material Command, USAF and the Advanced Research Projects Agency (ARPA) under grant number F33615-93-1-1330. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Wright Laboratory or the US Government.

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{pstone,veloso}@cs.cmu.edu

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