  The significance of the freshness of sensor and control data at the receiver
side, often referred to as Age of Information (AoI), is fundamentally
constrained by contention for limited network resources. Evidently, network
congestion is detrimental for AoI, where this congestion is partly self-induced
by the sensor transmission process in addition to the contention from other
transmitting sensors. In this work, we devise a decentralized AoI-minimizing
transmission policy for a number of sensor agents sharing capacity-limited,
non-FIFO duplex channels that introduce random delays in communication with a
common receiver. By implementing the same policy, however with no explicit
inter-agent communication, the agents minimize the expected AoI in this
partially observable system. We cater to the partial observability due to
random channel delays by designing a bootstrap particle filter that
independently maintains a belief over the AoI of each agent. We also leverage
mean-field control approximations and reinforcement learning to derive scalable
and optimal solutions for minimizing the expected AoI collaboratively.
