Competitive relative performance evaluation of neural controllers for competitive game playing with teams of real mobile robots

In this research, we describe the evolutionary training of artificial neural network controllers for competitive team game playing behaviors by teams of real mobile robots (The EvBots). During training (evolution), performance of controllers was evaluated based on the results of competitive tournaments of games played between robots (controllers) in an evolving population. Competitive tournament fitness evaluation does not require a human designer to define specific intermediate behaviors for a complex robot task. Intermediate behavior selection and evaluation becomes an implicit part of winning or losing games in a tournament. The acquisition of behavior in this 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 controllers were evolved in a simulated environment using evolutionary training algorithms and were then transferred to real robots in a physical environment for validation. Evolutionary robotics makes use of several distinct types or levels of performance evaluation. The work presented here focuses on the competitive relative tournament ranking metric used to drive the evolutionary process. After a population has been evolved, a second metric is needed to evaluate the quality of acquired gameplaying skills. We use a post training evaluation method that compares the evolved controllers to hand coded knowledge-based controllers designed to perform the same task. In particular, a very poor controller, and high quality controller give us two points on a continuum that can be used to rank the evolved controller quality.