  With the rapid development of Mobile Edge Computing (MEC), various real-time
applications have been deployed to benefit people's daily lives. The
performance of these applications relies heavily on the freshness of collected
environmental information, which can be quantified by its Age of Information
(AoI). In the traditional definition of AoI, it is assumed that the status
information can be actively sampled and directly used. However, for many
MEC-enabled applications, the desired status information is updated in an
event-driven manner and necessitates data processing. To better serve these
applications, we propose a new definition of AoI and, based on the redefined
AoI, we formulate an online AoI minimization problem for MEC systems. Notably,
the problem can be interpreted as a Markov Decision Process (MDP), thus
enabling its solution through Reinforcement Learning (RL) algorithms.
Nevertheless, the traditional RL algorithms are designed for MDPs with
completely unknown system dynamics and hence usually suffer long convergence
times. To accelerate the learning process, we introduce Post-Decision States
(PDSs) to exploit the partial knowledge of the system's dynamics. We also
combine PDSs with deep RL to further improve the algorithm's applicability,
scalability, and robustness. Numerical results demonstrate that our algorithm
outperforms the benchmarks under various scenarios.
