# Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing

arXiv: https://arxiv.org/abs/2312.10418 · HTML: https://mlsanity.com/paper/2312.10418/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 16 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Cooperative Internet of UAVs: Distributed Trajectory Design by Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2007.14297/) (2020)
- [Survey on Multi-Access Edge Computing for Internet of Things Realization](https://mlsanity.com/paper/1805.06695/) (2018)
- [Age of Information-Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective](https://mlsanity.com/paper/1908.02047/) (2019)
- [Distributed Reinforcement Learning for Age of Information Minimization in Real-Time IoT Systems](https://mlsanity.com/paper/2104.01527/) (2021)
- [Distributed Reinforcement Learning for Privacy-Preserving Dynamic Edge Caching](https://mlsanity.com/paper/2110.10349/) (2021)
- [UAV-to-Device Underlay Communications: Age of Information Minimization by Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2003.05830/) (2020)
- [Cooperative Task Offloading and Block Mining in Blockchain-based Edge Computing with Multi-agent Deep Reinforcement Learning](https://mlsanity.com/paper/2109.14263/) (2021)
- [Decentralized Task Offloading in Edge Computing: A Multi-User Multi-Armed Bandit Approach](https://mlsanity.com/paper/2112.11818/) (2021)
- [Age-Delay Tradeoffs in Queueing Systems](https://mlsanity.com/paper/1911.05601/) (2019)
- [Optimizing Information Freshness Through Computation-Transmission Tradeoff and Queue Management in Edge Computing](https://mlsanity.com/paper/1912.02692/) (2019)
- [Age of Information for Multicast Transmission with Fixed and Random Deadlines in IoT Systems](https://mlsanity.com/paper/2003.08304/) (2020)
- [Analysis on Computation-Intensive Status Update in Mobile Edge Computing](https://mlsanity.com/paper/2002.06400/) (2020)

## Built on by (most cited first, 3 of 3)

- [Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories](https://mlsanity.com/paper/2404.14238/) (2024)
- [Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing](https://mlsanity.com/paper/2409.16832/) (2024)
- [Fractional Policy Gradients: Reinforcement Learning with Long-Term Memory](https://mlsanity.com/paper/2507.00073/) (2025)

## Abstract (arXiv)

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.