PD-FAC: Probability Density Factorized Multi-Agent Distributional Reinforcement Learning for Multi-Robot Reliable Search-Reference-Cited by-同舟云学术

PD-FAC: Probability Density Factorized Multi-Agent Distributional Reinforcement Learning for Multi-Robot Reliable Search

Published:2022-10 Issue:4 Volume:7 Page:8869-8876
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Sheng Wenda¹^ORCID,Guo Hongliang²^ORCID,Yau Wei-Yun²^ORCID,Zhou Yingjie³^ORCID

Affiliation:

1. School of Automation Engineering, University of Electronic Science and Technology of China (UESTC), Chengdu, China

2. Institute for Infocomm Research, A*STAR, Singapore, Singapore

3. College of Computer Science, Sichuan University, Chengdu, Sichuan, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/9831196/09816134.pdf?arnumber=9816134

Reference30 articles.

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2. QTRAN: Learning to factorize with transformation for cooperative multi-agent reinforcement learning;son;Proc Int Conf Mach Learn,0

3. Anytime guaranteed search using spanning trees;hollinger,2008

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