# Age-Based Scheduling for Mobile Edge Computing: A Deep Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2312.00279 · HTML: https://mlsanity.com/paper/2312.00279/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 77 references that are in the map.
- First posted on arXiv 1 Dec 2023.

## Builds on (most cited first, 19 of 19)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Mobile Edge Computing: A Survey on Architecture and Computation Offloading](https://mlsanity.com/paper/1702.05309/) (2017)
- [Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/2002.06215/) (2020)
- [Discounted Reinforcement Learning Is Not an Optimization Problem](https://mlsanity.com/paper/1910.02140/) (2019)
- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [On The Age Of Information In Status Update Systems With Packet Management](https://mlsanity.com/paper/1506.08637/) (2015)
- [Optimal Energy Allocation and Task Offloading Policy for Wireless Powered Mobile Edge Computing Systems](https://mlsanity.com/paper/1907.07565/) (2019)
- [Optimizing Information Freshness Through Computation-Transmission Tradeoff and Queue Management in Edge Computing](https://mlsanity.com/paper/1912.02692/) (2019)
- [Age Based Task Scheduling and Computation Offloading in Mobile-Edge Computing Systems](https://mlsanity.com/paper/1905.11570/) (2019)
- [Online Optimization of Wireless Powered Mobile-Edge Computing for Heterogeneous Industrial Internet of Things](https://mlsanity.com/paper/1909.02445/) (2019)
- [Optimizing Information Freshness in Computing enabled IoT Networks](https://mlsanity.com/paper/1910.05578/) (2019)
- [Joint Optimization of Sensing and Computation for Status Update in Mobile Edge Computing Systems](https://mlsanity.com/paper/2210.17025/) (2022)
- [Age of Processing-Based Data Offloading for Autonomous Vehicles in Multi-RATs Open RAN](https://mlsanity.com/paper/2308.07242/) (2023)
- [Optimizing Task-Specific Timeliness With Edge-Assisted Scheduling for Status Update](https://mlsanity.com/paper/2310.18895/) (2023)
- [Age of Processing: Age-driven Status Sampling and Processing Offloading for Edge Computing-enabled Real-time IoT Applications](https://mlsanity.com/paper/2003.10916/) (2020)
- [Age of Information in G/G/1/1 Systems](https://mlsanity.com/paper/1805.12586/) (2018)
- [Optimal Status Update for Caching Enabled IoT Networks: A Dueling Deep R-Network Approach](https://mlsanity.com/paper/2106.06945/) (2021)

## Built on by (most cited first, 6 of 6)

- ["X of Information'' Continuum: A Survey on AI-Driven Multi-dimensional Metrics for Next-Generation Networked Systems](https://mlsanity.com/paper/2507.19657/) (2025)
- [Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks](https://mlsanity.com/paper/2501.11267/) (2025)
- [ReaCritic: Reasoning Transformer-based DRL Critic-model Scaling For Wireless Networks](https://mlsanity.com/paper/2505.10992/) (2025)
- [Tera-SpaceCom: GNN-based Deep Reinforcement Learning for Joint Resource Allocation and Task Offloading in TeraHertz Band Space Networks](https://mlsanity.com/paper/2409.07911/) (2024)
- [Goal-oriented Transmission Scheduling: Structure-guided DRL with a Unified Dual On-policy and Off-policy Approach](https://mlsanity.com/paper/2501.11921/) (2025)
- [A Survey of Freshness-Aware Wireless Networking with Reinforcement Learning](https://mlsanity.com/paper/2512.21412/) (2025)

## Abstract (arXiv)

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.