UW-MARL: Multi-agent reinforcement learning for underwater adaptive sampling using autonomous vehicles

Mehdi Rahmati, Mohammad Nadeem, Vidyasagar Sadhu, Dario Pompili

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the International Conference on Underwater Networks and Systems, WUWNET 2019
PublisherICST
ISBN (Electronic)9781450377409
DOIs
StatePublished - Oct 23 2019
Event2019 International Conference on Underwater Networks and Systems, WUWNET 2019 - Atlanta, United States
Duration: Oct 23 2019Oct 25 2019

Publication series

NamePervasiveHealth: Pervasive Computing Technologies for Healthcare
ISSN (Print)2153-1633

Conference

Conference2019 International Conference on Underwater Networks and Systems, WUWNET 2019
Country/TerritoryUnited States
CityAtlanta
Period10/23/1910/25/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Computer Science Applications
  • Health Informatics

Keywords

  • Autonomous underwater vehicles
  • Field experiments
  • Multi-agent reinforcement learning
  • Underwater adaptive sampling

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