An adaptive cooperation with reinforcement learning for robot soccer games

摘要

A strategy system with self-improvement and self-learning abilities for robot soccer system has been developed in this study. This work focuses on the cooperation strategy for the task assignment and develops an adaptive cooperation method for this system. This method was inspired by reinforcement learning (RL) and game theory. The developed system includes two subsystems: the task assignment system and the RL system. The task assignment system assigns one of the four roles, Attacker, Helper, Defender, and Goalkeeper, to each separate robot with the same physical and mechanical conditions to achieve cooperation. The assigned role to robots considers the situation in the game field. Each role has its own behaviors and tasks. The RL helps the Helper and Defender to improve the ability of their policy selection on the real-time confrontation. The RL system can not only learn to figure up how Helper helps its teammates to form an attack or a defense type but also learn to stand a proper defensive strategy. Some experiments on FIRE simulator and standard platform have been demonstrated that the proposed method performs better than the competitors.