  Multi-agent reinforcement learning (MARL) has long been a significant and
everlasting research topic in both machine learning and control. With the
recent development of (single-agent) deep RL, there is a resurgence of
interests in developing new MARL algorithms, especially those that are backed
by theoretical analysis. In this paper, we review some recent advances a
sub-area of this topic: decentralized MARL with networked agents. Specifically,
multiple agents perform sequential decision-making in a common environment,
without the coordination of any central controller. Instead, the agents are
allowed to exchange information with their neighbors over a communication
network. Such a setting finds broad applications in the control and operation
of robots, unmanned vehicles, mobile sensor networks, and smart grid. This
review is built upon several our research endeavors in this direction, together
with some progresses made by other researchers along the line. We hope this
review to inspire the devotion of more research efforts to this exciting yet
challenging area.
