This article studies a multi-agent reinforcement learning algorithm based on agent action prediction. In multi-agent system, the action of learning agent selection is inevitably affected by the action of other agents, so the reinforcement learning system needs to consider the joint state and joint action of multi-agent based on this. In addition, the application of this method in the cooperative strategy learning of soccer robot is studied, so that the multi-agent system can pass through the environment. To realize the division of labour and cooperation of multi-robots, the interactive learning is used to master the behaviour strategy. Combined with the characteristics of decision-making of soccer robot, this article analyses the role transformation and experience sharing of multi-agent reinforcement learning, and applies it to the local attack strategy of soccer robot, uses this algorithm to learn the action selection strategy of the main robot in the team, and uses Matlab platform for simulation verification. The experimental results prove the effectiveness of the research method, and the superiority of the proposed method is validated compared with some simple methods.

中文翻译：

---

基于多智能体强化学习的足球机器人决策策略研究

本文研究了一种基于智能体动作预测的多智能体强化学习算法。在多智能体系统中，学习智能体选择的动作不可避免地会受到其他智能体的动作的影响，因此强化学习系统需要在此基础上考虑多智能体的联合状态和联合动作。此外，研究了该方法在足球机器人协作策略学习中的应用，使多智能体系统能够穿越环境。为了实现多机器人的分工协作，通过交互学习来掌握行为策略。本文结合足球机器人决策的特点，分析了多智能体强化学习的角色转换和经验分享，并将其应用于足球机器人的局部攻击策略，利用该算法学习球队主力机器人的动作选择策略，并利用Matlab平台进行仿真验证。实验结果证明了研究方法的有效性，并与一些简单的方法相比，验证了所提出方法的优越性。