Robot soccer for the study of learning and coordination issues in multi-agent systems

Abstract

The goal of the robotic soccer competition is to develop completely autonomous robotic agents that can cooperate to perform the desired tasks in an extremely dynamic environment. The approach is to train the robots, using various combinations of learning algorithms, i.e., the robots are to initially have no understanding of their environment. The robots are rewarded for positive contributions to the team, and in turn learn better performance through multiple sessions. The majority of learning will take place in simulation and then transferred to real physical mobile robots. The concept of tropism-based control architecture is introduced that not only allows for the evolution of cooperative strategies, but also obtains the acquired knowledge in a format that is easily comprehensible by humans. The results of many generations of simulated evolution are presented, accompanied by the game results and fitness characteristics.