ROLE DIVERSITY IN ROBOT SOCCER BASED ON REINFORCEMENT LEARNING

In this paper, the role diversity based on reinforcement learning in robot soccer is studied. Through simulation and analysis, it is shown that the Q algorithm infinite-horizon discounted model in is not suitable to this task. Instead of that, average-reward model is used for improving the algorithm. Simulation experiments show that the convergence rate in learning and the system performance are twice increased after improvement.