Multi-Objective-Optimization Assisted Data Collection Framework for IoUT Based on Offline Reinforcement
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916016543498240 |
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| author | Ding, Yimian Wang, Xinqi Xu, Jingzehua Xie, Guanwen Liu, Weiyi Li, Yi |
| author_facet | Ding, Yimian Wang, Xinqi Xu, Jingzehua Xie, Guanwen Liu, Weiyi Li, Yi |
| contents | The Information Updating Networks (IUNs) offers significant potential for ocean exploration but encounters challenges due to dynamic underwater environments and severe system attenuation. Current methods relying on Autonomous Underwater Vehicles (AUVs) based on online reinforcement learning (RL) lead to high computational costs and low data utilization. To address these issues and the constraints of turbulent ocean environments, we propose a multi-AUV assisted data collection framework for IUNs based on multi-agent offline RL. This framework maximizes data rate and the value of information (VoI), minimizes energy consumption, and ensures collision avoidance by utilizing environmental and equipment status data. We introduce a semi-communication decentralized training with decentralized execution (SC-DTDE) paradigm and a multi-agent independent conservative Q-learning algorithm (MAICQL) to effectively tackle the problem. Extensive simulations demonstrate the high applicability, robustness, and data collection efficiency of the proposed framework. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_11282 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Objective-Optimization Assisted Data Collection Framework for IoUT Based on Offline Reinforcement Ding, Yimian Wang, Xinqi Xu, Jingzehua Xie, Guanwen Liu, Weiyi Li, Yi Systems and Control The Information Updating Networks (IUNs) offers significant potential for ocean exploration but encounters challenges due to dynamic underwater environments and severe system attenuation. Current methods relying on Autonomous Underwater Vehicles (AUVs) based on online reinforcement learning (RL) lead to high computational costs and low data utilization. To address these issues and the constraints of turbulent ocean environments, we propose a multi-AUV assisted data collection framework for IUNs based on multi-agent offline RL. This framework maximizes data rate and the value of information (VoI), minimizes energy consumption, and ensures collision avoidance by utilizing environmental and equipment status data. We introduce a semi-communication decentralized training with decentralized execution (SC-DTDE) paradigm and a multi-agent independent conservative Q-learning algorithm (MAICQL) to effectively tackle the problem. Extensive simulations demonstrate the high applicability, robustness, and data collection efficiency of the proposed framework. |
| title | Multi-Objective-Optimization Assisted Data Collection Framework for IoUT Based on Offline Reinforcement |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.11282 |