# Distributed Reinforcement Learning in Multi-Agent Networked Systems

@article{Lin2020DistributedRL, title={Distributed Reinforcement Learning in Multi-Agent Networked Systems}, author={Yiheng Lin and Guannan Qu and Longbo Huang and Adam Wierman}, journal={ArXiv}, year={2020}, volume={abs/2006.06555} }

We study distributed reinforcement learning (RL) for a network of agents. The objective is to find localized policies that maximize the (discounted) global reward. In general, scalability is a challenge in this setting because the size of the global state/action space can be exponential in the number of agents. Scalable algorithms are only known in cases where dependencies are local, e.g., between neighbors. In this work, we propose a Scalable Actor Critic framework that applies in settings… CONTINUE READING

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