Spatio-temporal dependence-based tensor fusion for thermocline analysis in Argo data

Jiang, Yu; Lin, Shunfu; Ruan, Jinjian; Qi, Hong

doi:10.1177/0959651820933735

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…In Equation ( 12), ρ can be seen as a classifier that maps pairs of state samples to pairs of abstract states. The mutual information in (11) is not sensitive to the code order of random variables u and u , which mentions a potential caveat in the optimization objective in Equation (12); that is, we would like the random variable for the next abstract state to have the same meaning as the random variable for the abstract state. The transition operator T M can be applied sequentially to show the sequence of changes in the abstract state and effectively plan the abstract state transition model M. The loss function is obtained by bringing Equation (12) in Equation (11):…”

Section: Super Sampling Info-ganmentioning

confidence: 99%

“…The multi-AUV hunting, which refers to the capture of mobile targets (evaders) through collaboration between AUVs (the hunters) in unknown environments, is a typical case of multi-AUV coordination. It involves interdisciplinary knowledge such as navigation, control, communication, and cooperation among multiple intelligent agents [3][4][5][6][7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Wang

Sui

Qin

et al. 2023

JMSE

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Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.

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Section: Super Sampling Info-ganmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Wang

Sui

Qin

et al. 2023

JMSE

View full text Add to dashboard Cite

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Signal Recovery for Incomplete Ocean Data via Graph Signal Processing

Liao

et al. 2022

GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training

Wang

Qin

et al. 2022

JMSE

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This study aims to solve the issue of the safe navigation of autonomous underwater vehicles (AUVs) in an unknown underwater environment. AUV will encounter canyons, rocks, reefs, fish, and underwater vehicles that threaten its safety during underwater navigation. A game-based soft actor–critic (GSAC) path planning method is proposed in this study to improve the adaptive capability of autonomous planning and the reliability of obstacle avoidance in the unknown underwater environment. Considering the influence of the simulation environment, the obstacles in the simulation environment are regarded as agents and play a zero-sum game with the AUV. The zero-sum game problem is solved by improving the strategy of AUV and obstacles, so that the simulation environment evolves intelligently with the AUV path planning strategy. The proposed method increases the complexity and diversity of the simulation environment, enables AUV to train in a variable environment specific to its strategy, and improves the adaptability and convergence speed of AUV in unknown underwater environments. Finally, the Python language is applied to write an unknown underwater simulation environment for the AUV simulation testing. GSAC can guide the AUV to the target point in the unknown underwater environment while avoiding large and small static obstacles, canyons, and small dynamic obstacles. Compared with the soft actor–critic(SAC) and the deep Q-network (DQN) algorithm, GSAC has better adaptability and convergence speed in the unknown underwater environment. The experiments verifies that GSAC has faster convergence, better stability, and robustness in unknown underwater environments.

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Spatio-temporal dependence-based tensor fusion for thermocline analysis in Argo data

Cited by 3 publications

References 32 publications

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Signal Recovery for Incomplete Ocean Data via Graph Signal Processing

Autonomous Underwater Vehicle Path Planning Method of Soft Actor–Critic Based on Game Training

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