Twin attentive deep reinforcement learning for multi-agent defensive convoy-Reference-Cited by-同舟云学术

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Twin attentive deep reinforcement learning for multi-agent defensive convoy

Published:2022-12-27 Issue: Volume: Page:
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Fan Dongyu,Shen Haikuo,Dong Lijing

Funder

National Natural Science Foundation of China

Beijing Advanced Innovation Center for Intelligent Robots and Systems, Beijing Institute of Technology

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01759-5.pdf

Reference57 articles.

1. Kaur N, Kaur H (2022) A multi-agent based evacuation planning for disaster management: a narrative review. Arch Comput Methods Eng 29:4085–4113

2. Ben-Dor G, Ben-Elia E, Benenson I (2021) Population downscaling in multi-agent transportation simulations: a review and case study. Simul Model Pract Theory 108:102233

3. Amirkhani A, Barshooi AH (2021) Consensus in multi-agent systems: a review. Artif Intell Rev 55:3897–3935

4. Mahmoud MS (2020) Multiagent systems: introduction and coordination control. CRC Press, Boca Raton

5. Hasan YA, Garg A, Sugaya S, Tapia L (2020) Defensive escort teams for navigation in crowds via multi-agent deep reinforcement learning. IEEE Robot Autom Lett 5(4):5645–5652

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