  Multi-agent settings remain a fundamental challenge in the reinforcement
learning (RL) domain due to the partial observability and the lack of accurate
real-time interactions across agents. In this paper, we propose a new method
based on local communication learning to tackle the multi-agent RL (MARL)
challenge within a large number of agents coexisting. First, we design a new
communication protocol that exploits the ability of depthwise convolution to
efficiently extract local relations and learn local communication between
neighboring agents. To facilitate multi-agent coordination, we explicitly learn
the effect of joint actions by taking the policies of neighboring agents as
inputs. Second, we introduce the mean-field approximation into our method to
reduce the scale of agent interactions. To more effectively coordinate
behaviors of neighboring agents, we enhance the mean-field approximation by a
supervised policy rectification network (PRN) for rectifying real-time agent
interactions and by a learnable compensation term for correcting the
approximation bias. The proposed method enables efficient coordination as well
as outperforms several baseline approaches on the adaptive traffic signal
control (ATSC) task and the StarCraft II multi-agent challenge (SMAC).
