UW-MARL: Multi-Agent Reinforcement Learning for Underwater Adaptive Sampling using Autonomous Vehicles

被引:1
|
作者
Rahmati, Mehdi [1 ]
Nadeem, Mohammad [1 ]
Sadhu, Vidyasagar [1 ]
Pompili, Dario [1 ]
机构
[1] Rutgers State Univ, Dept Elect & Comp Engn, New Brunswick, NJ 08901 USA
基金
美国国家科学基金会;
关键词
Multi-agent reinforcement learning; underwater adaptive sampling; autonomous underwater vehicles; field experiments;
D O I
10.1145/3366486.3366533
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Near-real-time water-quality monitoring in uncertain environments such as rivers, lakes, and water reservoirs of different variables is critical to protect the aquatic life and to prevent further propagation of the potential pollution in the water. In order to measure the physical values in a region of interest, adaptive sampling is helpful as an energy- and time-efficient technique since an exhaustive search of an area is not feasible with a single vehicle. We propose an adaptive sampling algorithm using multiple autonomous vehicles, which are well-trained, as agents, in a Multi-Agent Reinforcement Learning (MARL) framework to make efficient sequence of decisions on the adaptive sampling procedure. The proposed solution is evaluated using experimental data, which is fed into a simulation framework. Experiments were conducted in the Raritan River, Somerset and in Carnegie Lake, Princeton, NJ during July 2019.
引用
收藏
页数:5
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