Improved Learning in Multi-Agent Pursuer-Evader UAV Scenarios via Mechanism Design and Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Improved Learning in Multi-Agent Pursuer-Evader UAV Scenarios via Mechanism Design and Deep Reinforcement Learning

Published:2024-06-04 Issue: Volume:64 Page:144-151
ISSN:
Container-title:2024 International Conference on Unmanned Aircraft Systems (ICUAS)
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Author:

Cabral Kleber¹,Delamer Jean-Alexis²,Rabbath Camille-Alain³,Lechevin Nicolas³,Williams Craig⁴,Givigi Sidney¹

Affiliation:

1. School of Computing, Ingenuity Labs Research Institute, Queen's University,Kingston,ON,Canada,K7L 3N6

2. Saint Francis Xavier University,Department of Computer Science,Antigonish,NS,Canada,B2G 2W5

3. Defence Research and Development Canada,Valcartier,Canada

4. Royal Military College of Canada,Department of Physics and Space Science,Kingston,ON,Canada,K7K 7B4

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10556795/10556824/10557110.pdf?arnumber=10557110

Reference17 articles.

1. Multi-Agent Inverse Reinforcement Learning

2. Multi-agent deep reinforcement learning: a survey

3. A Survey on Transfer Learning for Multiagent Reinforcement Learning Systems;Silva;Journal of Artificial Intelligence Research,2019

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