Abstract
Machine Learning is now an exciting field due to the increased computing power available today. Realworld application discoveries are increasing, but also new algorithms and strategies developed in this area. Games have always been a great playground for studying these new ideas and Geometry Friends is a great example. It is a multi-player puzzle game with its own competition that aims to distinguish the best cooperative and non-cooperative agents. The single player problem has had many different approaches over the years with very satisfactory results. Attention now turns to the resolution of the cooperation component associated with multi-player gameplay. In this paper, we propose a consistent multi-agent learning system architecture, inspired by the single agent success of using a weighted directed graph and Reinforcement Learning. The motivation is to build a solid foundation for future cooperation solutions that want to expand and exploit Machine Learning knowledge. The final results will demonstrate that our system outperforms all proposals submitted to date, based on a relative simple structure giving the complex demands of a multi-agent environment.