A Scoring Policy for Simulated Soccer Agents Using Reinforcement Learning

Abstract

The robotic soccer is one of the complex multi-agent systems in which agents play the role of soccer players. The characteristics of such systems are: real-time, noisy, collaborative and adversarial. Because of the inherent complexity of this type of systems, machine learning is used for training agents. Since the main purpose of a soccer game is to score goals, it is important for a robotic soccer agent to have a clear policy about whether s/he should attempt to score in a given situation. Many parameters affect the result of shooting toward the goal. UvA Trilearn simulation team considers two more important parameters for this behavior. This paper describes the optimizing policy which is used in the UvA team, by choosing two additional important parameters as well as using reinforcement learning method.