Heuristically Assisted Multiagent RL-Based Framework for Computation Offloading and Resource Allocation of Mobile-Edge Computing-Reference-Cited by-同舟云学术

Heuristically Assisted Multiagent RL-Based Framework for Computation Offloading and Resource Allocation of Mobile-Edge Computing

Published:2023-09-01 Issue:17 Volume:10 Page:15477-15487
ISSN:2327-4662
Container-title:IEEE Internet of Things Journal
language:
Short-container-title:IEEE Internet Things J.

Author:

Li Xulong¹^ORCID,Qin Yunhui²^ORCID,Huo Jiahao¹^ORCID,Huangfu Wei¹^ORCID

Affiliation:

1. Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services, University of Science and Technology Beijing, Beijing, China

2. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, China

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Computer Science Applications,Hardware and Architecture,Information Systems,Signal Processing

Link

http://xplorestaging.ieee.org/ielx7/6488907/10227658/10091485.pdf?arnumber=10091485

Reference32 articles.

1. Multiagent Deep Reinforcement Learning for Joint Multichannel Access and Task Offloading of Mobile-Edge Computing in Industry 4.0

2. Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks

3. Dynamic Task Offloading in Multi-Agent Mobile Edge Computing Networks

4. Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

5. Playing Atari with deep reinforcement learning;mnih;arXiv 1312 5602,2013

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