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

摘要

Soccer is a rich domain for the study of multiagent learning issues. Not only must the players learn low-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, namely shooting a moving ball. We then discuss the issues that arise as we extend the learning scenario to require collaborative and adversarial learning. 1 Introduction Soccer is a rich domain for the study of multiagent learning issues. Teams of players must work together in order to put the ball in the opposing goal while at the same time defending their own. Learning is essential in this task since the dynamics of the system can change as the opponents' behaviors chang...