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A multiagent system composed of autonomous surface vehicles is proposed herein to efficiently monitor the water quality. To achieve a safe control of the fleet, the fleet policy should be able to act based on measurements and fleet state. It is proposed to use local Gaussian processes and deep reinforcement learning to jointly obtain effective monitoring policies. Local Gaussian processes, unlike classical global Gaussian processes, can accurately model the information in a dissimilar spatial correlation which captures more accurately the water quality information. A deep convolutional policy is proposed, that bases the decisions on the observation on the mean and variance of this model, by means of an information gain reward. Using a double deep Q\u2010learning algorithm, agents are trained to minimize the estimation error in a safe manner thanks to a Consensus\u2010based heuristic. Simulation results indicate an improvement of up to 24% in terms of the mean absolute error with the proposed models. Also, training results with 1\u20133 agents indicate that our proposed approach returns 20% and 24% smaller average estimation errors for, respectively, monitoring water quality variables and monitoring algae blooms, as compared to state\u2010of\u2010the\u2010art approaches.<\/jats:p>","DOI":"10.1002\/aisy.202300850","type":"journal-article","created":{"date-parts":[[2024,4,26]],"date-time":"2024-04-26T21:39:20Z","timestamp":1714167560000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring"],"prefix":"10.1002","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7796-3599","authenticated-orcid":false,"given":"Samuel","family":"Yanes Luis","sequence":"first","affiliation":[{"name":"Department of Electronic Engineering University of Sevilla  41005 Sevilla Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitriy","family":"Shutin","sequence":"additional","affiliation":[{"name":"Institute of Navigation and Communications German Aerospace Center  82234 Wessling Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Marchal G\u00f3mez","sequence":"additional","affiliation":[{"name":"Institute of Navigation and Communications German Aerospace Center  82234 Wessling Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2481-5058","authenticated-orcid":false,"given":"Daniel","family":"Guti\u00e9rrez Reina","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering University of Sevilla  41005 Sevilla Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2612-0388","authenticated-orcid":false,"given":"Sergio","family":"Toral Mar\u00edn","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering University of Sevilla  41005 Sevilla Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,4,26]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoleng.2020.106086"},{"key":"e_1_2_10_3_1","unstructured":"S.Pieterkosky A.Ziegwied C.Cavanagh L.Thompson inOCEANS 2017 \u2013 Anchorage Alaska2017 pp.1\u20135."},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.scitotenv.2022.159741"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1088\/1755-1315\/821\/1\/012018"},{"key":"e_1_2_10_6_1","unstructured":"M. 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