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.

翻译：传感器与控制数据在接收端的新鲜度（常称为信息年龄，AoI）其重要性根本上受限于有限网络资源的竞争。显然，网络拥塞对AoI不利，这种拥塞除其他传输传感器的竞争外，部分由传感器自身的传输过程引起。本文针对多个共享容量受限、非先入先出双工通道（该通道在与公共接收器通信时引入随机延迟）的传感器智能体，设计了一种去中心化的AoI最小化传输策略。通过实施相同策略但无需智能体间显式通信，各智能体在该部分可观测系统中最小化期望AoI。我们通过设计自主维护各智能体AoI置信度的引导粒子滤波器，应对随机信道延迟导致的部分可观测性。同时，利用平均场控制近似和强化学习，推导出协同最小化期望AoI的可扩展最优解。