  This article reviews recent advances in multi-agent reinforcement learning
algorithms for large-scale control systems and communication networks, which
learn to communicate and cooperate. We provide an overview of this emerging
field, with an emphasis on the decentralized setting under different
coordination protocols. We highlight the evolution of reinforcement learning
algorithms from single-agent to multi-agent systems, from a distributed
optimization perspective, and conclude with future directions and challenges,
in the hope to catalyze the growing synergy among distributed optimization,
signal processing, and reinforcement learning communities.
