Multi-Agent Path Finding with Prioritized Communication Learning-Reference-Cited by-同舟云学术

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Multi-Agent Path Finding with Prioritized Communication Learning

Published:2022-05-23 Issue: Volume: Page:
ISSN:
Container-title:2022 International Conference on Robotics and Automation (ICRA)
language:
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Author:

Li Wenhao¹,Chen Hongjun¹,Jin Bo¹,Tan Wenzhe¹,Zha Hongyuan¹,Wang Xiangfeng¹

Affiliation:

1. School of Computer Science and Technology, East China Normal University,Shanghai,China,200062

Funder

National Key Research and Development Program of China

NSFC

STCSM

Fundamental Research Funds for the Central Universities

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9811522/9811357/09811643.pdf?arnumber=9811643

Reference45 articles.

1. Cluster based routing protocol for mobile ad hoc networks;rezaee;INFOCOM,2009

2. Social influence as intrinsic motivation for multi-agent deep reinforcement learning;jaques;ICML,2019

3. Bootstrap your own latent: A new approach to self-supervised learning;grill;NeurIPS,2020

4. Human-level control through deep reinforcement learning;mnih;Nature,2015

5. Learning neural network policies with guided policy search under unknown dynamics;levine;NeurIPS,2014

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1. Crowd Perception Communication-Based Multi- Agent Path Finding With Imitation Learning;IEEE Robotics and Automation Letters;2024-10

2. DHAA: Distributed heuristic action aware multi-agent path finding in high density scene;Multimedia Tools and Applications;2024-08-01

3. A decentralized path planning model based on deep reinforcement learning;Computers and Electrical Engineering;2024-07

4. Improved Communication and Collision-Avoidance in Dynamic Multi-Agent Path Finding;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

5. Enhanced Causal Reasoning and Graph Networks for Multi-agent Path Finding;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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