A Lightweight Deep Recurrent Q-Learning Technique for Autonomous Wildfire Surveillance

Jeremy Cantor; Patrick Kreidl; John Nuszkowski; Alan Harris; Ayan Dutta

doi:10.1109/ICMLA58977.2023.00164

A Lightweight Deep Recurrent Q-Learning Technique for Autonomous Wildfire Surveillance

Jeremy Cantor
, Patrick Kreidl
, John Nuszkowski
, Alan Harris
, Ayan Dutta

University of North Florida

Producción científica: Proceedings › revisión exhaustiva

Idioma original	English
Páginas	1100-1105
Número de páginas	6
DOI	https://doi.org/10.1109/ICMLA58977.2023.00164
Estado	Published - 2023
Evento	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 - Jacksonville, United States Duración: dic 15 2023 → dic 17 2023

Conference

Conference	22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
País/Territorio	United States
Ciudad	Jacksonville
Período	12/15/23 → 12/17/23

ASJC Scopus Subject Areas

Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

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10.1109/ICMLA58977.2023.00164

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title = "A Lightweight Deep Recurrent Q-Learning Technique for Autonomous Wildfire Surveillance",

keywords = "reinforcement learning, wildfire surveillance",

author = "Jeremy Cantor and Patrick Kreidl and John Nuszkowski and Alan Harris and Ayan Dutta",

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AU - Harris, Alan

AU - Dutta, Ayan

PY - 2023

Y1 - 2023

KW - reinforcement learning

KW - wildfire surveillance

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M3 - Proceedings

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T2 - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

Y2 - 15 December 2023 through 17 December 2023

ER -

A Lightweight Deep Recurrent Q-Learning Technique for Autonomous Wildfire Surveillance

Conference

ASJC Scopus Subject Areas

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