Multi-agent learning has gained increasing attention to tackle distributed machine learning scenarios under constrictions of data exchanging. However, existing multi-agent learning models usually consider data fusion under fixed and compulsory collaborative relations among agents, which is not as flexible and autonomous as human collaboration. To fill this gap, we propose a distributed multi-agent learning model inspired by human collaboration, in which the agents can autonomously detect suitable collaborators and refer to collaborators' model for better performance. To implement such adaptive collaboration, we use a collaboration graph to indicate the pairwise collaborative relation. The collaboration graph can be obtained by graph learning techniques based on model similarity between different agents. Since model similarity can not be formulated by a fixed graphical optimization, we design a graph learning network by unrolling, which can learn underlying similar features among potential collaborators. By testing on both regression and classification tasks, we validate that our proposed collaboration model can figure out accurate collaborative relationship and greatly improve agents' learning performance.

翻译：多智能体学习在处理数据交换受限的分布式机器学习场景中日益受到关注。然而，现有的大多数多智能体学习模型通常假设智能体之间存在固定且强制性的协作关系进行数据融合，这种方式不如人类协作那样灵活自主。为解决这一问题，我们提出了一种受人类协作启发的分布式多智能体学习模型，其中智能体能够自主检测合适的协作者，并参考协作者模型以提升自身性能。为了实现这种自适应协作，我们引入协作图来表示智能体两两之间的协作关系。该协作图可通过基于不同智能体间模型相似性的图学习技术获得。由于模型相似性无法通过固定的图优化形式化表达，我们设计了一种通过展开实现的图学习网络，该网络能够学习潜在协作者之间的隐含相似特征。通过在回归和分类任务上的测试，我们验证了所提出的协作模型能够准确识别协作关系，并显著提升智能体的学习性能。