  A dual-hop status update system aided by energy harvesting (EH) relays with
finite data and energy buffers is studied in this work. To achieve timely
status updates, the best relays should be selected to minimize the average age
of information (AoI), which is a recently proposed metric to evaluate
information freshness. The average AoI minimization can be formulated as a
Markov decision process (MDP), but the state space for capturing channel and
buffer evolution grows exponentially with the number of relays, leading to high
solution complexity. We propose a relay selection (RS) scheme based on deep
reinforcement learning (DRL) according to the instantaneous channel packet
freshness and buffer information of each relay. Simulation results show a
significant improvement of the proposed DRL-based RS scheme over state-of-art
approaches.
