Chinese Journal of Ship Research, Volume. 19, Issue 1, 256(2024)

Unmanned surface vehicle escape strategy based on hybrid sampling deep Q-network

Yuanpeng YANG1,2, Lifei SONG2, Jiaqi MAO2, Yi LI2, and Houjing CHEN2,3
Author Affiliations
  • 1Systems Engineering Research Institute, CSSC, Beijing 100094, China
  • 2Key Laboratory of High Performance Ship Technology of Ministry of Education, Wuhan University of Technology, Wuhan 430063, China
  • 3China Ship Development and Design Center, Wuhan 430064, China
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    References(10)

    [4] [4] THOMAS H, SIP S. Evolving behavial strategies in predat prey[J]. International Joint Conference on Artificial Intelligence, 2005: 113126.

    [11] [11] WANG Y D, DONG L, SUN C Y. Cooperative control f multiplayer pursuitevasion games with reinfcement learning[J]. Neurocomputing, 2019.

    [12] [12] CARSTEN H, THOMY P, THOMAS G, et al. Emergent escapebased flocking behavi using multiagent reinfcement learning[D]. America: Cnell University, 2019.

    [13] [13] XIONG H, CAO H H, ZHANG L, et al. A dynamics perspective of pursuitevasion games of intelligent agents with the ability to learn[D]. Ithaca, NY: Cnell University, 2021.

    [19] [19] MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing Atari with deep reinfcement learning[EBOL]. (20131219)[20220920]. https:arxiv.gabs1312.5602

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    Yuanpeng YANG, Lifei SONG, Jiaqi MAO, Yi LI, Houjing CHEN. Unmanned surface vehicle escape strategy based on hybrid sampling deep Q-network[J]. Chinese Journal of Ship Research, 2024, 19(1): 256

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    Paper Information

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    Received: Sep. 27, 2022

    Accepted: --

    Published Online: Mar. 18, 2025

    The Author Email:

    DOI:10.19693/j.issn.1673-3185.03105

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