Scalable Reinforcement Learning for Multiagent Networked Systems

We study reinforcement learning (RL) in a setting with a network of agents whose states and actions interact in a local manner where the objective is to find localized policies such that the (discounted) global reward is maximized. A fundamental challenge in this setting is that the state-action space size scales exponentially in the number of agents, rendering the problem intractable for large networks. In this paper, we propose a scalable actor critic (SAC) framework that exploits the network structure and finds a localized policy that is an O ( ρ κ + 1 ) -approximation of a stationary point of the objective for some ρ ∈ ( 0 , 1 ) , with complexity that scales with the local state-action space size of the largest κ -hop neighborhood of the network. We illustrate our model and approach using examples from wireless communication, epidemics, and traffic.