# Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning

arXiv: https://arxiv.org/abs/2008.02033 · HTML: https://mlsanity.com/paper/2008.02033/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [GA-DRL: Graph Neural Network-Augmented Deep Reinforcement Learning for DAG Task Scheduling over Dynamic Vehicular Clouds](https://mlsanity.com/paper/2307.00777/) (2023)
- [A Distributed Deep Reinforcement Learning Technique for Application Placement in Edge and Fog Computing Environments](https://mlsanity.com/paper/2110.12415/) (2021)
- [TFormer: A Transmission-Friendly ViT Model for IoT Devices](https://mlsanity.com/paper/2302.07734/) (2023)
- [Artificial Intelligence for Web 3.0: A Comprehensive Survey](https://mlsanity.com/paper/2309.09972/) (2023)
- [UniLCD: Unified Local-Cloud Decision-Making via Reinforcement Learning](https://mlsanity.com/paper/2409.11403/) (2024)
- [Generalizable Pareto-Optimal Offloading with Reinforcement Learning in Mobile Edge Computing](https://mlsanity.com/paper/2509.10474/) (2025)
- [FastAdaBelief: Improving Convergence Rate for Belief-based Adaptive Optimizers by Exploiting Strong Convexity](https://mlsanity.com/paper/2104.13790/) (2021)
- [Efficient Task Offloading Algorithm for Digital Twin in Edge/Cloud Computing Environment](https://mlsanity.com/paper/2307.05888/) (2023)
- [A Deep Learning Based Resource Allocator for Communication Networks with Dynamic User Utility Demands](https://mlsanity.com/paper/2311.04600/) (2023)
- [Digital Twin-assisted Reinforcement Learning for Resource-aware Microservice Offloading in Edge Computing](https://mlsanity.com/paper/2403.08687/) (2024)
- [A Hybrid Proactive And Predictive Framework For Edge Cloud Resource Management](https://mlsanity.com/paper/2511.16075/) (2025)

## Abstract (arXiv)

Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency. A fundamental problem in MEC is how to efficiently offload heterogeneous tasks of mobile applications from user equipment (UE) to MEC hosts. Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC hosts. However, these methods have weak adaptability to new environments because they have low sample efficiency and need full retraining to learn updated policies for new environments. To overcome this weakness, we propose a task offloading method based on meta reinforcement learning, which can adapt fast to new environments with a small number of gradient updates and samples. We model mobile applications as Directed Acyclic Graphs (DAGs) and the offloading policy by a custom sequence-to-sequence (seq2seq) neural network. To efficiently train the seq2seq network, we propose a method that synergizes the first order approximation and clipped surrogate objective. The experimental results demonstrate that this new offloading method can reduce the latency by up to 25% compared to three baselines while being able to adapt fast to new environments.