Parameter sharing, as an important technique in multi-agent systems, can effectively solve the scalability issue in large-scale agent problems. However, the effectiveness of parameter sharing largely depends on the environment setting. When agents have different identities or tasks, naive parameter sharing makes it difficult to generate sufficiently differentiated strategies for agents. Inspired by research pertaining to the brain in biology, we propose a novel parameter sharing method. It maps each type of agent to different regions within a shared network based on their identity, resulting in distinct subnetworks. Therefore, our method can increase the diversity of strategies among different agents without introducing additional training parameters. Through experiments conducted in multiple environments, our method has shown better performance than other parameter sharing methods.

翻译：参数共享作为多智能体系统中的重要技术，能有效解决大规模智能体问题中的可扩展性难题。然而参数共享的效果高度依赖于环境设定：当智能体具有不同身份或任务时，简单的参数共享难以生成足够差异化的智能体策略。受生物大脑研究的启发，我们提出了一种新颖的参数共享方法。该方法根据智能体的身份特征，将每种类型的智能体映射至共享网络中的不同区域，从而形成差异化的子网络。因此，我们的方法能在不引入额外训练参数的前提下，提升不同智能体之间的策略多样性。通过在多个环境中的实验验证，本方法展现出优于其他参数共享方法的性能表现。