The learning of agents in a competitive space such as a game is a challenging task. The aim of the proposed research is to improve the reinforcement learning techniques in a competitive multi-agent for the Polo game. First, the video dataset is prepared. Then, the rules of the Polo game are extracted as a class diagram. An architecture is designed for multi-agent team in the Polo game. Therefore, an algorithm is proposed for the temporal difference in the game belief space for improving reward catching. The reward function is implemented in the agent team. Finally, the research improvement is evaluated by increasing 31 units in comparison with previous work. Therefore, competitive learning in the agent team has been improved.

在竞争性空间（例如游戏）中学习代理人是一项艰巨的任务。拟议研究的目的是在Polo游戏的竞争性多智能体中改进强化学习技术。首先，准备视频数据集。然后，将Polo游戏的规则提取为类图。为Polo游戏中的多代理团队设计了一种体系结构。因此，提出了一种针对游戏信念空间中的时间差异的算法，以改善奖励的捕获。奖励功能是在代理团队中实现的。最后，与以前的工作相比，通过增加31个单元来评估研究的改进。因此，代理商团队中的竞争性学习得到了改善。