Proximal Policy Optimization Based Decentralized Networked Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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Proximal Policy Optimization Based Decentralized Networked Multi-Agent Reinforcement Learning

Published:2024-06-18 Issue: Volume:21 Page:839-844
ISSN:
Container-title:2024 IEEE 18th International Conference on Control & Automation (ICCA)
language:
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Author:

Liu Jinyi¹,Li Fangyu¹,Wang Jingjing¹,Han Honggui¹

Affiliation:

1. Engineering Research Center of Digital Community, Ministry of Education, and Beijing Artificial Intelligence Institute, Beijing University of Technology,Faculty of Information Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing,China,100124

Funder

National Key Research and Development Project

National Science Foundation of China

Beijing Natural Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10591777/10591797/10591960.pdf?arnumber=10591960

Reference20 articles.

1. Learning to communicate with deep multi-agent reinforcement learning;Foerster,2016

2. Monotonic value function factorisation for deep multi-agent reinforcement learning;Rashid;The Journal of Machine Learning Research,2020

3. A review of cooperative multi-agent deep reinforcement learning

4. Multi-Agent Reinforcement Learning: A Review of Challenges and Applications

5. Multi-agent actor-critic for mixed cooperative-competitive environments;Lowe;Advances in neural information processing systems,2017

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