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.

我们提出了一种新颖的参数共享方法，通过将每种类型的代理映射到共享网络中的不同区域，基于它们的身份，形成不同的子网络，从而增加了不同代理之间的策略多样性，而不引入额外的训练参数，通过在多个环境中进行的实验证明，我们的方法表现优于其他参数共享方法。

多智能体强化学习的自适应参数共享