  The time average expected age of information (AoI) is studied for status
updates sent from an energy-harvesting transmitter with a finite-capacity
battery. The optimal scheduling policy is first studied under different
feedback mechanisms when the channel and energy harvesting statistics are
known. For the case of unknown environments, an average-cost reinforcement
learning algorithm is proposed that learns the system parameters and the status
update policy in real time. The effectiveness of the proposed methods is
verified through numerical results.
