Using Monte Carlo Search With Data Aggregation to Improve Robot Soccer Policies
This work addresses the specific problem of enhancing robot soccer performance in adversarial, dynamic settings, representing an incremental improvement over existing methods.
The paper tackled the problem of adapting robot soccer policies for a defender robot to opponent strategies in the challenging RoboCup environment, resulting in improved ball interceptions, reduced opponent goals, and more efficient team positioning as tested in simulations and on real robots.
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.