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