Mobile edge computing (MEC) is a promising paradigm for real-time applications with intensive computational needs (e.g., autonomous driving), as it can reduce the processing delay. In this work, we focus on the timeliness of computational-intensive updates, measured by Age-ofInformation (AoI), and study how to jointly optimize the task updating and offloading policies for AoI with fractional form. Specifically, we consider edge load dynamics and formulate a task scheduling problem to minimize the expected time-average AoI. The uncertain edge load dynamics, the nature of the fractional objective, and hybrid continuous-discrete action space (due to the joint optimization) make this problem challenging and existing approaches not directly applicable. To this end, we propose a fractional reinforcement learning(RL) framework and prove its convergence. We further design a model-free fractional deep RL (DRL) algorithm, where each device makes scheduling decisions with the hybrid action space without knowing the system dynamics and decisions of other devices. Experimental results show that our proposed algorithms reduce the average AoI by up to 57.6% compared with several non-fractional benchmarks.

翻译：移动边缘计算作为一种新兴范式，能够降低处理延迟，因而在需要密集计算能力的实时应用（如自动驾驶）中具有重要前景。本研究聚焦于计算密集型更新的时效性（以信息年龄衡量），并探索如何联合优化更新任务与卸载策略以最小化信息年龄的分数阶形式。具体而言，我们考虑边缘负载动态特性，构建了一个任务调度问题，旨在最小化期望的时间平均信息年龄。边缘负载的不确定性、分数阶目标函数的固有特性以及混合连续-离散动作空间（源于联合优化需求）使该问题极具挑战性，现有方法难以直接适用。为此，我们提出了一种分数阶强化学习框架并证明了其收敛性，进而设计了一种无模型的分数阶深度强化学习算法。该算法使各设备可在无需获知系统动态及其他设备决策的情况下，基于混合动作空间独立制定调度决策。实验结果表明，与多种非分数阶基准方法相比，所提算法可将平均信息年龄降低多达57.6%。