The multi-agent system is the optimal solution to complex intelligent problems. In accordance with the game theory, the concept of loyalty is introduced to analyze the relationship between agents' individual income and global benefits and build the logical architecture of the multi-agent system. Besides, to verify the feasibility of the method, the cyclic neural network is optimized, the bi-directional coordination network is built as the training network for deep learning, and specific training scenes are simulated as the training background. After a certain number of training iterations, the model can learn simple strategies autonomously. Also, as the training time increases, the complexity of learning strategies rises gradually. Strategies such as obstacle avoidance, firepower distribution and collaborative cover are adopted to demonstrate the achievability of the model. The model is verified to be realizable by the examples of obstacle avoidance, fire distribution and cooperative cover. Under the same resource background, the model exhibits better convergence than other deep learning training networks, and it is not easy to fall into the local endless loop. Furthermore, the ability of the learning strategy is stronger than that of the training model based on rules, which is of great practical values.

中文翻译：

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基于博弈论的多智能体系统在双向协调网络模型中的应用

多智能体系统是复杂智能问题的最优解。根据博弈论，引入忠诚度的概念来分析代理人个人收入与全局利益的关系，构建多代理人系统的逻辑架构。此外，为了验证该方法的可行性，对循环神经网络进行了优化，构建了双向协调网络作为深度学习的训练网络，并模拟了特定的训练场景作为训练背景。经过一定次数的训练迭代后，模型可以自主学习简单的策略。此外，随着训练时间的增加，学习策略的复杂性逐渐上升。诸如避障之类的策略，采用火力分配和协同掩护来证明模型的可实现性。通过避障、火力分布、协同掩护等实例验证了该模型的可实现性。在相同资源背景下，该模型表现出比其他深度学习训练网络更好的收敛性，不易陷入局部死循环。此外，学习策略的能力强于基于规则的训练模型，具有很大的实用价值。并且不容易陷入局部死循环。此外，学习策略的能力强于基于规则的训练模型，具有很大的实用价值。并且不容易陷入局部死循环。此外，学习策略的能力强于基于规则的训练模型，具有很大的实用价值。