A Proposal of QLearning to Control the Attack of a 2D Robot Soccer Simulation Team

Abstract

This document presents a novel approach to control the attack behavior of a team of simulated soccer playing robot of the Robocup 2D category. The presented approach modifies the behavior of each player only when in the state "controlling the ball". The approach is based on a modified Q-Learning algorithm that implements a continuous machine learning process. After an initial learning phase, each player uses its previous experience during the simulation of the game to decide if it should dribble, pass or kick the ball to the adversary goal. Simulation results show that the proposed approach is capable of learn how to score goals, even when faced with the-champion of the previous world championship.