CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization

Iliya Kulbaka; Ayan Dutta; Ladislau Boloni; O. Patrick Kreidl; Swapnoneel Roy

doi:10.1109/ICMLA58977.2023.00157

CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization

Iliya Kulbaka, Ayan Dutta, Ladislau Boloni, O. Patrick Kreidl, Swapnoneel Roy

Producción científica: Literary contribution

Idioma original	English
Título de la publicación alojada	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023
Editores	M. Arif Wani, Mihai Boicu, Moamar Sayed-Mouchaweh, Pedro Henriques Abreu, Joao Gama
Páginas	1060-1065
Número de páginas	6
ISBN (versión digital)	9798350345346
DOI	https://doi.org/10.1109/ICMLA58977.2023.00157
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

Serie de la publicación

Nombre	Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 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

Acceder al documento

10.1109/ICMLA58977.2023.00157

Otros archivos y enlaces

Citar esto

Kulbaka, I., Dutta, A., Boloni, L., Kreidl, O. P., & Roy, S. (2023). CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization. En M. Arif Wani, M. Boicu, M. Sayed-Mouchaweh, P. H. Abreu, & J. Gama (Eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023 (pp. 1060-1065). (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). https://doi.org/10.1109/ICMLA58977.2023.00157

CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization. / Kulbaka, Iliya; Dutta, Ayan; Boloni, Ladislau et al.
Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. ed. / M. Arif Wani; Mihai Boicu; Moamar Sayed-Mouchaweh; Pedro Henriques Abreu; Joao Gama. 2023. p. 1060-1065 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

Producción científica: Literary contribution

Kulbaka, I, Dutta, A, Boloni, L, Kreidl, OP & Roy, S 2023, CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization. En M Arif Wani, M Boicu, M Sayed-Mouchaweh, PH Abreu & J Gama (eds.), Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, pp. 1060-1065, 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023, Jacksonville, United States, 12/15/23. https://doi.org/10.1109/ICMLA58977.2023.00157

Kulbaka I, Dutta A, Boloni L, Kreidl OP, Roy S. CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization. En Arif Wani M, Boicu M, Sayed-Mouchaweh M, Abreu PH, Gama J, editores, Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. 2023. p. 1060-1065. (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023). doi: 10.1109/ICMLA58977.2023.00157

Kulbaka, Iliya ; Dutta, Ayan ; Boloni, Ladislau et al. / CNN-LSTM-Based Deep Recurrent Q-Learning for Robotic Gas Source Localization. Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023. editor / M. Arif Wani ; Mihai Boicu ; Moamar Sayed-Mouchaweh ; Pedro Henriques Abreu ; Joao Gama. 2023. pp. 1060-1065 (Proceedings - 22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023).

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