Multiagent Reinforcement Learning Based Distributed Channel Access for Industrial Edge-Cloud Web 3.0-Reference-Cited by-同舟云学术

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Multiagent Reinforcement Learning Based Distributed Channel Access for Industrial Edge-Cloud Web 3.0

Published:2024-09 Issue:5 Volume:11 Page:3943-3954
ISSN:2327-4697
Container-title:IEEE Transactions on Network Science and Engineering
language:
Short-container-title:IEEE Trans. Netw. Sci. Eng.

Author:

Yang Chen¹^ORCID,Wang Yushi²^ORCID,Lan Shulin³^ORCID,Zhu Liehuang¹^ORCID

Affiliation:

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

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

3. School of Economics and Management, University of Chinese Academy of Sciences, Beijing, China

Funder

National Key R&D Program of China

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6488902/10637788/10472689.pdf?arnumber=10472689

Reference25 articles.

1. Towards product customization and personalization in IoT-enabled cloud manufacturing

2. Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learning

3. Multi-Agent Reinforcement Learning Based Channel Access Scheme for Underwater Optical Wireless Communication Networks

4. Maximizing Throughput of Aerial Base Stations via Resources-based Multi-Agent Proximal Policy Optimization: A Deep Reinforcement Learning Approach

5. The surprising effectiveness of PPO in cooperative, multi-agent games;Yu;Adv. Neural Inf. Process. Syst.,2022

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1. Efficient Scheduling of Energy-Constrained Tasks in Internet of Things Edge Computing Networks;International Journal of Swarm Intelligence Research;2024-08-07

2. A Multi-Agent Reinforcement Learning-Based Grant-Free Random Access Protocol for mMTC Massive MIMO Networks;Journal of Sensor and Actuator Networks;2024-04-30

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