# Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1804.00514 · HTML: https://mlsanity.com/paper/1804.00514/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2018.

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

- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [A Fast Task Offloading Optimization Framework for IRS-Assisted Multi-Access Edge Computing System](https://mlsanity.com/paper/2307.08474/) (2023)
- [Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply](https://mlsanity.com/paper/2102.05449/) (2021)
- [A Deep RL Approach on Task Placement and Scaling of Edge Resources for Cellular Vehicle-to-Network Service Provisioning](https://mlsanity.com/paper/2305.09832/) (2023)
- [The Two Faces of AI in Green Mobile Computing: A Literature Review](https://mlsanity.com/paper/2308.04436/) (2023)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)

## Abstract (arXiv)

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.