POAQL: A Partially Observable Altruistic Q-Learning Method for Cooperative Multi-Agent Reinforcement Learning-Reference-Cited by-同舟云学术

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POAQL: A Partially Observable Altruistic Q-Learning Method for Cooperative Multi-Agent Reinforcement Learning

Published:2024-05-13 Issue: Volume:13 Page:15076-15082
ISSN:
Container-title:2024 IEEE International Conference on Robotics and Automation (ICRA)
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Author:

Tao Lesong¹,Kang Miao¹,Dong Jinpeng¹,Zhang Songyi¹,Ye Ke¹,Chen Shitao¹,Zheng Nanning¹

Affiliation:

1. Xi’an Jiaotong University,National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10609961/10609862/10610745.pdf?arnumber=10610745

Reference33 articles.

1. Feasibility study: Moving non-homogeneous teams in congested video game environments;Ma

2. Coordinating hundreds of cooperative, autonomous vehicles in warehouses;Wurman;AI magazine,2008

3. Lifelong Multi-Agent Path Finding in Large-Scale Warehouses

4. Cobots: Robust symbiotic autonomous mobile service robots;Veloso

5. Multi-agent pathfinding: Definitions, variants, and benchmarks;Stern

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