Keepaway soccer: A machine learning testbed

Abstract

RoboCup simulated soccer presents many challenges to machine learning (ML) methods, including a large state space, hidden and uncertain state, multiple agents, and long and variable delays in the effects of actions. While there have been many successful ML applications to portions of the robotic soccer task, it appears to be still beyond the capabilities of modern machine learning techniques to enable a team of 11 agents to successfully learn the full robotic soccer task from sensors to actuators. Because the successful applications to portions of the task have been embedded in different teams and have often addressed different subtasks, they have been difficult to compare. We put forth keepaway soccer as a domain suitable for directly comparing different machine learning approaches to robotic soccer. It is complex enough that it can't be solved trivially, yet simple enough that complete machine learning approaches are feasible. In keepaway, one team, "the keepers," tries to keep control of the ball for as long as possible despite the efforts of "the takers." The keepers learn individually when to hold the ball and when to pass to a teammate, while the takers learn when to charge the ball-holder and when to cover possible passing lanes. We fully specify the domain and summarize some initial, successful learning results. © 2002 Springer-Verlag Berlin Heidelberg.