Scaling Up Multi-Agent Reinforcement Learning: An Extensive Survey on Scalability Issues-Reference-Cited by-同舟云学术

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Scaling Up Multi-Agent Reinforcement Learning: An Extensive Survey on Scalability Issues

Published:2024 Issue: Volume:12 Page:94610-94631
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Liu Dingbang¹^ORCID,Ren Fenghui¹^ORCID,Yan Jun¹,Su Guoxin¹^ORCID,Gu Wen²^ORCID,Kato Shohei³^ORCID

Affiliation:

1. Department of Computing and Information Technology, University of Wollongong, Wollongong, NSW, Australia

2. Center for Innovative Distance Education and Research, Japan Advanced Institute of Science and Technology, Nomi, Ishikawa, Japan

3. Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Aichi, Japan

Funder

Ministry of Education, Culture, Sports, Science and Technology-Japan, Grant–in–Aid for Scientific Research

commissioned research by National Institute of Information and Communications Technology (NICT), Japan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/6287639/10380310/10550936.pdf?arnumber=10550936

Reference135 articles.

1. Long Short-Term Memory-Networks for Machine Reading

2. Human-level control through deep reinforcement learning

3. Multi-agent deep reinforcement learning: a survey

4. A hybrid decision support system for managing humanitarian relief chains

5. Markov games as a framework for multi-agent reinforcement learning

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