Deep Reinforcement Learning Approach for UAV-Assisted Mobile Edge Computing Networks

Sangwon Hwang, Juseong Park, Hoon Lee, Mintae Kim, Inkyu Lee

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


This paper studies a deep reinforcement learning (DRL) approach for the unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks where a UAV-mounted server offloads computation tasks of mobile users (MUs). We aim at minimizing the energy consumption of the MUs by adjusting UAV mobility, UAV-MU association, computation resource allocation, and task offloading rules. This requires an online and joint optimization of different types of variables constructing heterogeneous solution spaces. To realize real-time optimization strategies, we propose an online DRL method based on the twin-delayed deep deterministic policy gradient (TD3) framework. The joint optimization of heterogeneous action variables is tackled by a novel actor neural network that partitions the high-dimensional action set into several solution spaces. In addition, the proposed TD3 framework achieves adaptability to new task offloading requests through our proposed training and execution strategy. Numerical results verify the effectiveness of the proposed DRL architecture over benchmark schemes.

Original languageEnglish
Pages (from-to)3839-3844
Number of pages6
JournalProceedings - IEEE Global Communications Conference, GLOBECOM
Publication statusPublished - 2022
Event2022 IEEE Global Communications Conference, GLOBECOM 2022 - Virtual, Online, Brazil
Duration: 2022 Dec 42022 Dec 8

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing


Dive into the research topics of 'Deep Reinforcement Learning Approach for UAV-Assisted Mobile Edge Computing Networks'. Together they form a unique fingerprint.

Cite this