# Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1805.06146 · HTML: https://mlsanity.com/paper/1805.06146/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2018.

## Built on by (most cited first, 26 of 26)

- [RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN](https://mlsanity.com/paper/2111.06978/) (2021)
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- [SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments](https://mlsanity.com/paper/2205.10635/) (2022)
- [Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design](https://mlsanity.com/paper/2101.09137/) (2021)
- [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)
- [CLARA: A Constrained Reinforcement Learning Based Resource Allocation Framework for Network Slicing](https://mlsanity.com/paper/2111.08397/) (2021)
- [BLAST: A Stealthy Backdoor Leverage Attack against Cooperative Multi-Agent Deep Reinforcement Learning based Systems](https://mlsanity.com/paper/2501.01593/) (2025)
- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [Online Learning for Orchestration of Inference in Multi-User End-Edge-Cloud Networks](https://mlsanity.com/paper/2202.10541/) (2022)
- [Exploring Deep Reinforcement Learning for Holistic Smart Building Control](https://mlsanity.com/paper/2301.11510/) (2023)
- [Structure-aware reinforcement learning for node-overload protection in mobile edge computing](https://mlsanity.com/paper/2107.01025/) (2021)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Adaptive Edge Offloading for Image Classification Under Rate Limit](https://mlsanity.com/paper/2208.00485/) (2022)
- [Optimizing Edge Offloading Decisions for Object Detection](https://mlsanity.com/paper/2410.18919/) (2024)
- [Task Graph offloading via Deep Reinforcement Learning in Mobile Edge Computing](https://mlsanity.com/paper/2309.10569/) (2023)
- [A Proactive Management Scheme for Data Synopses at the Edge](https://mlsanity.com/paper/2107.10558/) (2021)
- [Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding](https://mlsanity.com/paper/2110.01262/) (2021)
- [DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing](https://mlsanity.com/paper/2110.01863/) (2021)
- [Attention-Based Model and Deep Reinforcement Learning for Distribution of Event Processing Tasks](https://mlsanity.com/paper/2112.03835/) (2021)
- [Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning](https://mlsanity.com/paper/2209.08947/) (2022)
- [FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations](https://mlsanity.com/paper/2209.14399/) (2022)
- [Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment](https://mlsanity.com/paper/2307.05888/) (2023)
- [Intelligent Hybrid Resource Allocation in MEC-assisted RAN Slicing Network](https://mlsanity.com/paper/2405.17436/) (2024)
- [Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions](https://mlsanity.com/paper/2604.25018/) (2026)

## Abstract (arXiv)

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is a promising paradigm by providing computing capabilities in close proximity within a sliced radio access network (RAN), which supports both traditional communication and MEC services. Nevertheless, the design of computation offloading policies for a virtual MEC system remains challenging. Specifically, whether to execute a computation task at the mobile device or to offload it for MEC server execution should adapt to the time-varying network dynamics. In this paper, we consider MEC for a representative mobile user in an ultra-dense sliced RAN, where multiple base stations (BSs) are available to 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 maximize the long-term utility performance whereby an offloading decision is made based on the task queue state, the energy queue state as well as the channel qualities between MU and BSs. To break the curse of high dimensionality in state space, we first propose a double deep Q-network (DQN) based strategic computation offloading algorithm to learn the optimal policy without knowing a priori knowledge of network dynamics. Then motivated by the additive structure of the utility function, a Q-function decomposition technique is combined with the double DQN, which leads to novel learning algorithm for the solving of stochastic computation offloading. Numerical experiments show that our proposed learning algorithms achieve a significant improvement in computation offloading performance compared with the baseline policies.