Multi-agent Reinforcement Learning-based Resource Allocation Scheme for UAV-assisted Internet of Remote Things Systems

Author:

Lee Donggu1ORCID,Sun Young Ghyu2ORCID,Kim Soo Hyun2,Kim Jae-Hyun3ORCID,Shin Yoan4ORCID,Kim Dong In5ORCID,Kim Jin Young2ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, North Carolina State University, NC, USA

2. Department of Electronic Convergence Engineering, Kwangwoon University, Seoul, South Korea

3. Department of Electrical and Computer Engineering, Ajou University, Suwon, South Korea

4. School of Electronic Engineering, Soongsil University, Seoul, South Korea

5. Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, South Korea

Funder

Ministry of Science and ICT, South Korea

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference36 articles.

1. Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey

2. Nash Q-learning for general-sum stochastic games;hu;J Mach Learn Res,2003

3. Fully decentralized multi-agent reinforcement learning with networked agents;zhang;Proc Int Conf Mach Learn,2018

4. Multi-agent reinforcement learning: A selective overview of theories and algorithms;zhang;arXiv 1911 10635,2019

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2. Impact of Proactive LEO Satellite Tracking on Heterogeneous IoRT Network Performance;2023 XVIII International Symposium Problems of Redundancy in Information and Control Systems (REDUNDANCY);2023-10-24

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