# Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems

arXiv: https://arxiv.org/abs/2005.02459 · HTML: https://mlsanity.com/paper/2005.02459/

## Key facts

- Cited by 20 papers in the map (data through 13 Oct 2026).
- First posted on arXiv 10 Apr 2020.

## Built on by (most cited first, 20 of 20)

- [UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approach](https://mlsanity.com/paper/2106.15734/) (2021)
- [DeViT: Decomposing Vision Transformers for Collaborative Inference in Edge Devices](https://mlsanity.com/paper/2309.05015/) (2023)
- [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)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Multi-Agent RL-Based Industrial AIGC Service Offloading over Wireless Edge Networks](https://mlsanity.com/paper/2405.02972/) (2024)
- [Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence](https://mlsanity.com/paper/2201.11410/) (2022)
- [Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks](https://mlsanity.com/paper/2406.16994/) (2024)
- [FAuNO: Semi-Asynchronous Federated Reinforcement Learning Framework for Task Offloading in Edge Systems](https://mlsanity.com/paper/2506.02668/) (2025)
- [Multi-Turn Reasoning LLMs for Task Offloading in Mobile Edge Computing](https://mlsanity.com/paper/2604.07148/) (2026)
- [DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing](https://mlsanity.com/paper/2110.01863/) (2021)
- [Minimizing Age of Information for Mobile Edge Computing Systems: A Nested Index Approach](https://mlsanity.com/paper/2307.01366/) (2023)
- [QECO: A QoE-Oriented Computation Offloading Algorithm based on Deep Reinforcement Learning for Mobile Edge Computing](https://mlsanity.com/paper/2311.02525/) (2023)
- [Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing](https://mlsanity.com/paper/2403.08687/) (2024)
- [Computation Offloading for Multi-server Multi-access Edge Vehicular Networks: A DDQN-based Method](https://mlsanity.com/paper/2404.07215/) (2024)
- [Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing](https://mlsanity.com/paper/2409.16832/) (2024)
- [Multi-Agent Reinforcement Learning for Task Offloading in Wireless Edge Networks](https://mlsanity.com/paper/2509.01257/) (2025)
- [Deep Reinforcement Learning-driven Edge Offloading for Latency-constrained XR pipelines](https://mlsanity.com/paper/2603.16823/) (2026)
- [Analog RF Computing: A New Paradigm for Energy-Efficient Edge AI Over MU-MIMO Systems](https://mlsanity.com/paper/2605.14331/) (2026)
- [Heterogeneous Tasks Offloading in Vehicular Edge Computing: A Federated Meta Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2605.18437/) (2026)
- [PPO-STGNN: A Proximal Policy Optimization Approach with Spatio-Temporal Graph Neural Networks for DAG Task Scheduling in Cloud-Edge-End Computing](https://mlsanity.com/paper/2609.03503/) (2026)

## Abstract (arXiv)

In mobile edge computing systems, an edge node may have a high load when a large number of mobile devices offload their tasks to it. Those offloaded tasks may experience large processing delay or even be dropped when their deadlines expire. Due to the uncertain load dynamics at the edge nodes, it is challenging for each device to determine its offloading decision (i.e., whether to offload or not, and which edge node it should offload its task to) in a decentralized manner. In this work, we consider non-divisible and delay-sensitive tasks as well as edge load dynamics, and formulate a task offloading problem to minimize the expected long-term cost. We propose a model-free deep reinforcement learning-based distributed algorithm, where each device can determine its offloading decision without knowing the task models and offloading decision of other devices. To improve the estimation of the long-term cost in the algorithm, we incorporate the long short-term memory (LSTM), dueling deep Q-network (DQN), and double-DQN techniques. Simulation results with 50 mobile devices and five edge nodes show that the proposed algorithm can reduce the ratio of dropped tasks and average task delay by 86.4%-95.4% and 18.0%-30.1%, respectively, when compared with several existing algorithms.