  Age of Information (AoI) reflects the time that is elapsed from the
generation of a packet by a 5G user equipment(UE) to the reception of the
packet by a controller. A design of an AoI-aware radio resource scheduler for
UEs via reinforcement learning is proposed in this paper. In this paper, we
consider a remote control environment in which a number of UEs are transmitting
time-sensitive measurements to a remote controller. We consider the AoI
minimization problem and formulate the problem as a trade-off between
minimizing the sum of the expected AoI of all UEs and maximizing the throughput
of the network. Inspired by the success of machine learning in solving large
networking problems at low complexity, we develop a reinforcement
learning-based method to solve the formulated problem. We used the
state-of-the-art proximal policy optimization algorithm to solve this problem.
Our simulation results showthat the proposed algorithm outperforms the
considered baselines in terms of minimizing the expected AoI while maintaining
the network throughput.
