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AUV Surfacing Control With Adversarial Attack Against DLaaS Framework

Authors :
Zhang, Tianze
Miao, Xuhong
Li, Yibin
Jia, Lei
Zhuang, Yinghao
Source :
IEEE Transactions on Computers; February 2024, Vol. 73 Issue: 2 p327-339, 13p
Publication Year :
2024

Abstract

In this study, we consider surfacing control problems for the autonomous underwater vehicle in three-dimensional space under emergencies. Most model-based controllers cannot effectively solve these problems due to the unknown task environment model. Moreover, existing deep reinforcement learning (DRL) methods have some limitations, such as slow convergence or overestimation bias. For these purposes, we propose a model-free DRL algorithm based on the Deep Deterministic Policy Gradient within the paradigm of deep learning as a service (DLaaS). The algorithm combines existing expert episodes as demonstration and transitions from the interaction between the agent and task environment, which potentially reduce risk from adversarial attack. We propose a variable delay learning mechanism to improve the performance of the proposed algorithm. The simulation results show that our method can converge faster and has a more robust performance with adversarial attack than the existing DRL method under the premise of solving the AUV surfacing control tasks for safety.

Details

Language :
English
ISSN :
00189340 and 15579956
Volume :
73
Issue :
2
Database :
Supplemental Index
Journal :
IEEE Transactions on Computers
Publication Type :
Periodical
Accession number :
ejs65213307
Full Text :
https://doi.org/10.1109/TC.2021.3072072