Samuel Yanes Luis
Orcid: 0000-0002-7796-3599
According to our database1,
Samuel Yanes Luis
authored at least 15 papers
between 2020 and 2024.
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Bibliography
2024
Deep Reinforcement Multiagent Learning Framework for Information Gathering with Local Gaussian Processes for Water Monitoring.
Adv. Intell. Syst., August, 2024
Towards an Autonomous Surface Vehicle Prototype for Artificial Intelligence Applications of Water Quality Monitoring.
CoRR, 2024
Deep Reinforcement Multi-agent Learning framework for Information Gathering with Local Gaussian Processes for Water Monitoring.
CoRR, 2024
Decoupling Patrolling Tasks for Water Quality Monitoring: A Multi-Agent Deep Reinforcement Learning Approach.
IEEE Access, 2024
Informative Deep Reinforcement Path Planning for Heterogeneous Autonomous Surface Vehicles in Large Water Resources.
IEEE Access, 2024
Deep Variational Auto-Encoder for Model-Based Water Quality Patrolling with Intelligent Surface Vehicles.
Proceedings of the Advances in Artificial Intelligence, 2024
2023
Deep Reinforcement Learning Applied to Multi-agent Informative Path Planning in Environmental Missions.
Proceedings of the Mobile Robot: Motion Control and Path Planning, 2023
Censored deep reinforcement patrolling with information criterion for monitoring large water resources using Autonomous Surface Vehicles.
Appl. Soft Comput., 2023
2022
An evolutionary multi-objective path planning of a fleet of ASVs for patrolling water resources.
Eng. Appl. Artif. Intell., 2022
Maximum Information Coverage and Monitoring Path Planning with Unmanned Surface Vehicles Using Deep Reinforcement Learning.
Proceedings of the Optimization and Learning - 5th International Conference, 2022
2021
A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors.
Sensors, 2021
A Multiagent Deep Reinforcement Learning Approach for Path Planning in Autonomous Surface Vehicles: The Ypacaraí Lake Patrolling Case.
IEEE Access, 2021
Monitoring Water Resources through a Bayesian Optimization-based Approach using Multiple Surface Vehicles: The Ypacarai Lake Case Study.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
A Sample-Efficiency Comparison Between Evolutionary Algorithms and Deep Reinforcement Learning for Path Planning in an Environmental Patrolling Mission.
Proceedings of the IEEE Congress on Evolutionary Computation, 2021
2020
A Deep Reinforcement Learning Approach for the Patrolling Problem of Water Resources Through Autonomous Surface Vehicles: The Ypacarai Lake Case.
IEEE Access, 2020