Decentralized Reinforcement Learning for Timely Estimation in Multi-Hop Wireless Networks

The core message of this paper is to devise efficient decentralized policies that minimize both age of information (AoI) and estimation error in multi-hop wireless networks, while ensuring scalability through a graphical multi-agent reinforcement learning framework.