  The collaboration between agents has gradually become an important topic in
multi-agent systems. The key is how to efficiently solve the credit assignment
problems. This paper introduces MGAN for collaborative multi-agent
reinforcement learning, a new algorithm that combines graph convolutional
networks and value-decomposition methods. MGAN learns the representation of
agents from different perspectives through multiple graph networks, and
realizes the proper allocation of attention between all agents. We show the
amazing ability of the graph network in representation learning by visualizing
the output of the graph network, and therefore improve interpretability for the
actions of each agent in the multi-agent system.
