  We consider an IoT sensing network with multiple users, multiple energy
harvesting sensors, and a wireless edge node acting as a gateway between the
users and sensors. The users request for updates about the value of physical
processes, each of which is measured by one sensor. The edge node has a cache
storage that stores the most recently received measurements from each sensor.
Upon receiving a request, the edge node can either command the corresponding
sensor to send a status update, or use the data in the cache. We aim to find
the best action of the edge node to minimize the average long-term cost which
trade-offs between the age of information and energy consumption. We propose a
practical reinforcement learning approach that finds an optimal policy without
knowing the exact battery levels of the sensors.
