Observatorio de I+D+i UPM

Descripción
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.
Internacional	Si
Nombre congreso	2019 27th European Signal Processing Conference (EUSIPCO)
Tipo de participación	970
Lugar del congreso	A Coruña
Revisores	Si
ISBN o ISSN	978-9-0827-9703-9
DOI	10.23919/EUSIPCO.2019.8903067
Fecha inicio congreso	02/09/2019
Fecha fin congreso	06/09/2019
Desde la página	1
Hasta la página	5
Título de las actas	2019 27th European Signal Processing Conference (EUSIPCO)

Esta actividad pertenece a memorias de investigación

• Autor: David Baldazo (Universidad Politécnica de Madrid)
• Autor: Juan Parras Moral (UPM)
• Autor: Santiago Zazo Bello (UPM)

• Creador: Centro o Instituto I+D+i: Centro de I+d+i en Procesado de la Información y Telecomunicaciones
• Grupo de Investigación: Grupo de Aplicaciones del Procesado de Señal (GAPS)
• Departamento: Señales, Sistemas y Radiocomunicaciones