%AQu, Guannan%AWierman, Adam%ALi, Na%BJournal Name: Operations Research
%D2022%I
%JJournal Name: Operations Research
%K
%MOSTI ID: 10324690
%PMedium: X
%TScalable Reinforcement Learning for Multiagent Networked Systems
%XWe 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 [Formula: see text]-approximation of a stationary point of the objective for some [Formula: see text], with complexity that scales with the local state-action space size of the largest [Formula: see text]-hop neighborhood of the network. We illustrate our model and approach using examples from wireless communication, epidemics, and traffic.
%0Journal Article