Safe Multi-Agent Reinforcement Learning for Wireless Applications Against Adversarial Communications-Reference-Cited by-同舟云学术

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Safe Multi-Agent Reinforcement Learning for Wireless Applications Against Adversarial Communications

Published:2024 Issue: Volume:19 Page:6824-6839
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Lv Zefang¹^ORCID,Xiao Liang¹^ORCID,Chen Yifan¹,Chen Haoyu²^ORCID,Ji Xiangyang³^ORCID

Affiliation:

1. Department of Informatics and Communication Engineering and the Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Xiamen University, Xiamen, China

2. Institute of Artificial Intelligence, Xiamen University, Xiamen, China

3. Department of Automation, Tsinghua University, 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)

Link

http://xplorestaging.ieee.org/ielx8/10206/10319981/10584557.pdf?arnumber=10584557

Reference42 articles.

1. Multi-Agent Reinforcement Learning for Wireless Networks Against Adversarial Communications

2. Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

3. Multi-Agent Reinforcement Learning Based UAV Swarm Communications Against Jamming

4. The emergence of adversarial communication in multi-agent reinforcement learning;Blumenkamp

5. Collaborative Multiagent Reinforcement Learning Aided Resource Allocation for UAV Anti-Jamming Communication

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