Evolution of neural controllers for competitive game playing with teams of mobile robots

Abstract

In this work, we describe the evolutionary training of artificial neural network controllers for competitive team game playing behaviors by teams of real mobile robots. This research emphasized the development of methods to automate the production of behavioral robot controllers. We seek methods that do not require a human designer to define specific intermediate behaviors for a complex robot task. The work made use of a real mobile robot colony (EVolutionary roBOTs) and a closely coupled computer-based simulated training environment. The acquisition of behavior in an evolutionary robotics system was demonstrated using a robotic version of the game Capture the Flag. In this game, played by two teams of competing robots, each team tries to defend its own goal while trying to ‘attack’ another goal defended by the other team. Robot neural controllers relied entirely on processed video data for sensing of their environment. Robot controllers were evolved in a simulated environment using evolutionary training algorithms. In the evolutionary process, each generation consisted of a competitive tournament of games played between the controllers in an evolving population. Robot controllers were selected based on whether they won or lost games in the course of a tournament. Following a tournament, the neural controllers were ranked competitively according to how many games they won and the population was propagated using a mutation and replacement strategy. After several hundred generations, the best performing controllers were transferred to teams of real mobile robots, where they exhibited behaviors similar to those seen in simulation including basic navigation, the ability to distinguish between different types of objects, and goal tending behaviors.