Potential field hierarchical reinforcement learning approach for target search by multi-AUV in 3-D underwater environments-Reference-Cited by-同舟云学术

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Potential field hierarchical reinforcement learning approach for target search by multi-AUV in 3-D underwater environments

Published:2018-10-08 Issue:7 Volume:93 Page:1677-1683
ISSN:0020-7179
Container-title:International Journal of Control
language:en
Short-container-title:International Journal of Control

Author:

Cao Xiang¹²,Sun Hongbing¹,Guo Liqiang³

Affiliation:

1. School of Physics and Electronic Electrical Engineering, Huaiyin Normal University, Huaian, People's Republic of China

2. School of Automation, Southeast University, Nanjin, People's Republic of China

3. School of Computer Science and Technology, Huaiyin Normal University, Huaian, People's Republic of China

Funder

China Postdoctoral Science Foundation

National Natural Science Foundation of China

Natural Science Foundation of Jiangsu Province

Jiangsu Planned Projects for Postdoctoral Research Funds

Key University Science Research Project of Jiangsu Province

Publisher

Informa UK Limited

Subject

Computer Science Applications,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207179.2018.1526414

Reference22 articles.

1. A Hierarchical Reinforcement Learning Based Approach for Multi-robot Cooperation in Unknown Environments

2. Multi-AUV Cooperative Target Search Algorithm in 3-D Underwater Workspace

3. Multi-AUV target searching under ocean current based on PPSO and velocity synthesis algorithm

4. A NOVEL ARTIFICIAL POTENTIAL FIELD-BASED REINFORCEMENT LEARNING FOR MOBILE ROBOTICS IN AMBIENT INTELLIGENCE

5. Multi-robot Cooperation Based on Hierarchical Reinforcement Learning

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3. Iterative learning–based model-free adaptive precise heading following of an autonomous underwater vehicle with unknown disturbances;Transactions of the Institute of Measurement and Control;2024-02-12

4. Evaluation and learning in two-player symmetric games via best and better responses;Information Sciences;2023-11

5. State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment;Journal of Marine Science and Engineering;2023-06-21

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