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.

中文翻译：

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机器人足球比赛与强化学习的自适应合作

本研究开发了一种具有自我完善和自我学习能力的机器人足球系统策略系统。这项工作侧重于任务分配的合作策略，并为该系统开发了一种自适应合作方法。这种方法的灵感来自强化学习 (RL) 和博弈论。开发的系统包括两个子系统：任务分配系统和强化学习系统。任务分配系统将攻击者、帮助者、防守者和守门员四种角色中的一种分配给每个具有相同物理和机械条件的独立机器人，以实现协作。分配给机器人的角色考虑了比赛场地的情况。每个角色都有自己的行为和任务。RL 帮助 Helper 和 Defender 提高他们在实时对抗中的策略选择能力。RL 系统不仅可以学习了解 Helper 如何帮助其队友形成攻击或防御类型，还可以学习站立正确的防御策略。在 FIRE 模拟器和标准平台上的一些实验表明，该方法的性能优于竞争对手。