Article
A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors
Author/s | Yanes Luis, Samuel
Gutiérrez Reina, Daniel Toral, S. L. |
Department | Universidad de Sevilla. Departamento de Ingeniería Electrónica |
Publication Date | 2021 |
Deposit Date | 2021-06-23 |
Published in |
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Abstract | The monitoring of water resources using Autonomous Surface Vehicles with water-quality
sensors has been a recent approach due to the advances in unmanned transportation technology.
The Ypacaraí Lake, the biggest water ... The monitoring of water resources using Autonomous Surface Vehicles with water-quality sensors has been a recent approach due to the advances in unmanned transportation technology. The Ypacaraí Lake, the biggest water resource in Paraguay, suffers from a major contamination problem because of cyanobacteria blooms. In order to supervise the blooms using these on-board sensor modules, a Non-Homogeneous Patrolling Problem (a NP-hard problem) must be solved in a feasible amount of time. A dimensionality study is addressed to compare the most common methodologies, Evolutionary Algorithm and Deep Reinforcement Learning, in different map scales and fleet sizes with changes in the environmental conditions. The results determined that Deep Q-Learning overcomes the evolutionary method in terms of sample-efficiency by 50–70% in higher resolutions. Furthermore, it reacts better than the Evolutionary Algorithm in high space-state actions. In contrast, the evolutionary approach shows a better efficiency in lower resolutions and needs fewer parameters to synthesize robust solutions. This study reveals that Deep Q-learning approaches exceed in efficiency for the Non-Homogeneous Patrolling Problem but with many hyper-parameters involved in the stability and convergence. |
Project ID. | RTI2018-098964-B-I00
US-1257508 PY18-RE0009 PAIDI TIC 201 |
Citation | Yanes Luis, S., Gutiérrez Reina, D. y Toral Marín, S. (2021). A Dimensional Comparison between Evolutionary Algorithm and Deep Reinforcement Learning Methodologies for Autonomous Surface Vehicles with Water Quality Sensors. Sensors, 21 (8), Article number 2862. |
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