  It has long been recognized that multi-agent reinforcement learning (MARL)
faces significant scalability issues due to the fact that the size of the state
and action spaces are exponentially large in the number of agents. In this
paper, we identify a rich class of networked MARL problems where the model
exhibits a local dependence structure that allows it to be solved in a scalable
manner. Specifically, we propose a Scalable Actor-Critic (SAC) method that can
learn a near optimal localized policy for optimizing the average reward with
complexity scaling with the state-action space size of local neighborhoods, as
opposed to the entire network. Our result centers around identifying and
exploiting an exponential decay property that ensures the effect of agents on
each other decays exponentially fast in their graph distance.
