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A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case.

, , and . IEEE Access, (2020)

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A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case., , and . IEEE Access, (2021)An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources., , , , , and . Eng. Appl. Artif. Intell., (2022)Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring., , , , and . CoRR, (2024)A Sample-Efficiency Comparison Between Evolutionary Algorithms and Deep Reinforcement Learning for Path Planning in an Environmental Patrolling Mission., , , and . CEC, page 71-78. IEEE, (2021)Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study., , , and . CEC, page 1511-1518. IEEE, (2021)Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles., , and . Appl. Soft Comput., (2023)Censored Deep Reinforcement Patrolling with Information Criterion for Monitoring Large Water Resources using Autonomous Surface Vehicles., , and . CoRR, (2022)A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case., , and . IEEE Access, (2020)Maximum Information Coverage and Monitoring Path Planning with Unmanned Surface Vehicles Using Deep Reinforcement Learning., , and . OLA, volume 1684 of Communications in Computer and Information Science, page 13-24. Springer, (2022)A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors., , and . Sensors, 21 (8): 2862 (2021)