Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications-Reference-Cited by-同舟云学术

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Farthest Agent Selection With Episode-Wise Observations for Real-Time Multi-Agent Reinforcement Learning Applications

Published:2024 Issue: Volume:12 Page:108504-108514
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lee Hyunsoo¹^ORCID,Kim Gyu Seon¹^ORCID,Choi Minseok²^ORCID,Baek Hankyul¹^ORCID,Park Soohyun³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Korea University, Seoul, Republic of Korea

2. Department of Electronic Engineering, Kyung Hee University, Yongin, Republic of Korea

3. Division of Computer Science, Sookmyung Women’s University, Seoul, Republic of Korea

Funder

National Research Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10623680.pdf?arnumber=10623680

Reference36 articles.

1. Multi-Intersection Management for Connected Autonomous Vehicles by Reinforcement Learning

2. Collaborative Multi-Agent Deep Reinforcement Learning for Energy-Efficient Resource Allocation in Heterogeneous Mobile Edge Computing Networks

3. Multi-Agent Reinforcement Learning-Based Resource Allocation for UAV Networks

4. Multi-Agent Distributed Reinforcement Learning for Making Decentralized Offloading Decisions

5. Single and Multi-Agent Deep Reinforcement Learning for AI-Enabled Wireless Networks: A Tutorial

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