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Descripción
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| Multi-Agent Deep Reinforcement Learning is becoming a promising approach to the problem of coordination of swarms of drones in dynamic systems. In particular, the use of autonomous aircraft for flood monitoring is now regarded as an economically viable option and it can benefit from this kind of automation: swarms of unmanned aerial vehicles could autonomously generate nearly real-time inundation maps that could improve relief work planning. In this work, we study the use of Deep Q-Networks (DQN) as the optimization strategy for the trajectory planning that is required for monitoring floods, we train agents over simulated floods in procedurally generated terrain and demonstrate good performance with two different reward schemes. | |
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Internacional
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Si |
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Nombre congreso
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2019 27th European Signal Processing Conference (EUSIPCO) |
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Tipo de participación
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970 |
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Lugar del congreso
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A Coruña |
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Revisores
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Si |
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ISBN o ISSN
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978-9-0827-9703-9 |
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DOI
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10.23919/EUSIPCO.2019.8903067 |
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Fecha inicio congreso
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02/09/2019 |
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Fecha fin congreso
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06/09/2019 |
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Desde la página
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1 |
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Hasta la página
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5 |
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Título de las actas
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2019 27th European Signal Processing Conference (EUSIPCO) |