  Learning to coordinate actions among agents is essential in complicated
multi-agent systems. Prior works are constrained mainly by the assumption that
all agents act simultaneously, and asynchronous action coordination between
agents is rarely considered. This paper introduces a bi-level multi-agent
decision hierarchy for coordinated behavior planning. We propose a novel
election mechanism in which we adopt a graph convolutional network to model the
interaction among agents and elect a first-move agent for asynchronous
guidance. We also propose a dynamically weighted mixing network to effectively
reduce the misestimation of the value function during training. This work is
the first to explicitly model the asynchronous multi-agent action coordination,
and this explicitness enables to choose the optimal first-move agent. The
results on Cooperative Navigation and Google Football demonstrate that the
proposed algorithm can achieve superior performance in cooperative
environments. Our code is available at
\url{https://github.com/Amanda-1997/EFA-DWM}.
