# Reinforcement Learning-Empowered Mobile Edge Computing for 6G Edge Intelligence

arXiv: https://arxiv.org/abs/2201.11410 · HTML: https://mlsanity.com/paper/2201.11410/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 190 references that are in the map.
- First posted on arXiv 27 Jan 2022.

## Builds on (most cited first, 30 of 46)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Deep Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2002.00444/) (2020)
- [Applications of Deep Reinforcement Learning in Communications and Networking: A Survey](https://mlsanity.com/paper/1810.07862/) (2018)
- [A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems](https://mlsanity.com/paper/1902.10265/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial](https://mlsanity.com/paper/2011.03615/) (2020)
- [Deep Reinforcement Learning for Autonomous Internet of Things: Model, Applications and Challenges](https://mlsanity.com/paper/1907.09059/) (2019)
- [Optimized Computation Offloading Performance in Virtual Edge Computing Systems via Deep Reinforcement Learning](https://mlsanity.com/paper/1805.06146/) (2018)
- [Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing](https://mlsanity.com/paper/2009.11277/) (2020)
- [Path Planning for UAV-Mounted Mobile Edge Computing with Deep Reinforcement Learning](https://mlsanity.com/paper/2001.10268/) (2020)
- [Deep Reinforcement Learning for Task Offloading in Mobile Edge Computing Systems](https://mlsanity.com/paper/2005.02459/) (2020)
- [Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing](https://mlsanity.com/paper/1911.03887/) (2019)
- [Learning-Based Computation Offloading for IoT Devices with Energy Harvesting](https://mlsanity.com/paper/1712.08768/) (2017)
- [Deep Reinforcement Learning for Stochastic Computation Offloading in Digital Twin Networks](https://mlsanity.com/paper/2011.08430/) (2020)
- [Lyapunov-guided Deep Reinforcement Learning for Stable Online Computation Offloading in Mobile-Edge Computing Networks](https://mlsanity.com/paper/2010.01370/) (2020)
- [Delay-Optimal Computation Task Scheduling for Mobile-Edge Computing Systems](https://mlsanity.com/paper/1604.07525/) (2016)
- [Multi-Tenant Cross-Slice Resource Orchestration: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/1807.09350/) (2018)
- [Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks](https://mlsanity.com/paper/2010.01722/) (2020)
- [Security in Mobile Edge Caching with Reinforcement Learning](https://mlsanity.com/paper/1801.05915/) (2018)
- [Multi-Agent Meta-Reinforcement Learning for Self-Powered and Sustainable Edge Computing Systems](https://mlsanity.com/paper/2002.08567/) (2020)
- [Next Generation Cloud Computing: New Trends and Research Directions](https://mlsanity.com/paper/1707.07452/) (2017)
- [Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing](https://mlsanity.com/paper/1703.06060/) (2017)
- [A Comprehensive Survey on Fog Computing: State-of-the-art and Research Challenges](https://mlsanity.com/paper/1710.11001/) (2017)
- [Stacked Auto Encoder Based Deep Reinforcement Learning for Online Resource Scheduling in Large-Scale MEC Networks](https://mlsanity.com/paper/2001.09223/) (2020)
- [Offloading and Resource Allocation with General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2002.08119/) (2020)
- [A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks](https://mlsanity.com/paper/2007.10102/) (2020)
- [Network Slicing for 5G with SDN/NFV: Concepts, Architectures and Challenges](https://mlsanity.com/paper/1703.04676/) (2017)
- [Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning](https://mlsanity.com/paper/1804.00514/) (2018)
- [Towards Big data processing in IoT: Path Planning and Resource Management of UAV Base Stations in Mobile-Edge Computing System](https://mlsanity.com/paper/1906.05023/) (2019)

## Built on by (most cited first, 2 of 2)

- [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)
- [NetworkGym: Reinforcement Learning Environments for Multi-Access Traffic Management in Network Simulation](https://mlsanity.com/paper/2411.04138/) (2024)

## Abstract (arXiv)

Mobile edge computing (MEC) is considered a novel paradigm for computation-intensive and delay-sensitive tasks in fifth generation (5G) networks and beyond. However, its uncertainty, referred to as dynamic and randomness, from the mobile device, wireless channel, and edge network sides, results in high-dimensional, nonconvex, nonlinear, and NP-hard optimization problems. Thanks to the evolved reinforcement learning (RL), upon iteratively interacting with the dynamic and random environment, its trained agent can intelligently obtain the optimal policy in MEC. Furthermore, its evolved versions, such as deep RL (DRL), can achieve higher convergence speed efficiency and learning accuracy based on the parametric approximation for the large-scale state-action space. This paper provides a comprehensive research review on RL-enabled MEC and offers insight for development in this area. More importantly, associated with free mobility, dynamic channels, and distributed services, the MEC challenges that can be solved by different kinds of RL algorithms are identified, followed by how they can be solved by RL solutions in diverse mobile applications. Finally, the open challenges are discussed to provide helpful guidance for future research in RL training and learning MEC.