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Age of Information (AoI) measures the freshness of the information at aremote location. AoI reflects the time that is elapsed since the generation ofthe packet by a transmitter. In this paper, we consider a remote monitoringproblem (e.g., remote factory) in which a number of sensor nodes aretransmitting time sensitive measurements to a remote monitoring site. Weconsider minimizing a metric that maintains a trade-off between minimizing thesum of the expected AoI of all sensors and minimizing an Ultra Reliable LowLatency Communication (URLLC) term. The URLLC term is considered to ensure thatthe probability the AoI of each sensor exceeds a predefined threshold isminimized. Moreover, we assume that sensors tolerate different threshold valuesand generate packets at different sizes. Motivated by the success of machinelearning in solving large networking problems at low complexity, we develop alow complexity reinforcement learning based algorithm to solve the proposedformulation. We trained our algorithm using the state-of-the-art actor-criticalgorithm over a set of public bandwidth traces. Simulation results show thatthe proposed algorithm outperforms the considered baselines in terms ofminimizing the expected AoI and the threshold violation of each sensor.

Reinforcement Learning Based Scheduling Algorithm for Optimizing Age of Information in Ultra Reliable Low Latency Networks