The Simple Soccer Machine Learning Environment

The RoboCup simulated soccer league is a dynamic, complex and uncertain environment which presents many challenges to machine learning techniques. The asynchronous design of the RoboCup simulation environment can create long and unpredictable delays in the effects of actions, often causing onerous training times. A new environment known as Simple Soccer is proposed which, while retaining much of the dynamics and complexity of RoboCup, provides more certainty and less delay, thus increasing the viability of machine learning techniques. The goal of this work is to create an environment complex and dynamic enough that while low-level tactics may differ due to the removal of systematic uncertainty, high-level strategies directly applicable to the RoboCup environment can be developed. This paper specifies the Simple Soccer environment, and presents some initial learning results.