Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies

RoboCup soccer competitions are considered among the most challenging multi-robot adversarial environments, due to their high dynamism and the partial observability of the environment. In this paper we introduce a method based on a combination of Monte Carlo search and data aggregation (MCSDA) to adapt discrete-action soccer policies for a defender robot to the strategy of the opponent team. By exploiting a simple representation of the domain, a supervised learning algorithm is trained over an initial collection of data consisting of several simulations of human expert policies. Monte Carlo policy rollouts are then generated and aggregated to previous data to improve the learned policy over multiple epochs and games. The proposed approach has been extensively tested both on a soccer-dedicated simulator and on real robots. Using this method, our learning robot soccer team achieves an improvement in ball interceptions, as well as a reduction in the number of opponents' goals. Together with a better performance, an overall more efficient positioning of the whole team within the field is achieved

RoboCup: the evolution of a robotic scientific challenge

The RoboCup is a scientific challenge used to foster research in the robotics areas, which main objective consists in developing a robotic football team able to play against a human team in the year 2050. This paper describes the rules of such a competition, the actual state of the art of robotic football players in the middle size league, and describes the main characteristics to take into account in order to build such type of robots. These aspects are described and analysed in terms of further developments.Fundação para a Ciência e a Tecnologia (FCT) - projecto “Development of Robotic Football Team for participation in the RoboCup (Middle Size League)”, POSI/ROBO/43892/2002