Zobrazeno 1 - 10
of 860
pro vyhledávání: '"P. Artaud"'
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of communication approaches in MARL. While multiple communication method
Externí odkaz:
http://arxiv.org/abs/2401.15059
Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the context of intelligent vehicle control, RL has made impressive progress
Externí odkaz:
http://arxiv.org/abs/2311.02746
Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. Despite notable advances, there are still problems that require investigation. The lazy agent pathology is a famous problem in MARL that
Externí odkaz:
http://arxiv.org/abs/2311.02741
Autor:
Elise Artaud-Macari, Emeline Fresnel, Adrien Kerfourn, Clémence Roussel, David Debeaumont, Marie-Anne Melone, Francis-Edouard Gravier, Tristan Bonnevie, Mathieu Salaün, Antoine Cuvelier, Christophe Girault
Publikováno v:
BMC Pulmonary Medicine, Vol 24, Iss 1, Pp 1-10 (2024)
Abstract Background The ventilatory physiopathology of patients with interstitial lung disease (ILD) remains poorly understood. We aimed to personalize a mechanical simulator to model healthy and ILD profiles ventilation, and to evaluate the effect o
Externí odkaz:
https://doaj.org/article/b48c6415046e46a1b8322af5d8f23ad2
In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may end up learning sub-optimal policies, not contributing to the objecti
Externí odkaz:
http://arxiv.org/abs/2306.11846
Publikováno v:
2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), Guayaquil, Ecuador, 2023, pp. 1-8
Artificial Intelligence has been used to help human complete difficult tasks in complicated environments by providing optimized strategies for decision-making or replacing the manual labour. In environments including multiple agents, such as football
Externí odkaz:
http://arxiv.org/abs/2303.15471
When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team. Such agents end up learning sub-optimal policies, demonstrating undesired lazy behaviour
Externí odkaz:
http://arxiv.org/abs/2303.14227
Autor:
Artaud, Alexandre, Rougemaille, Nicolas, Vlaic, Sergio, Renard, Vincent T., Atodiresei, Nicolae, Coraux, Johann
Publikováno v:
Physical Review B, 106, L201402 (2022)
Unlike conventional two-dimensional (2D) semiconductor superlattices, moir\'{e} patterns in 2D materials are flexible and their electronic, magnetic, optical, and mechanical properties depend on their topography. Within a continuous+atomistic theory
Externí odkaz:
http://arxiv.org/abs/2211.02482
Autor:
Yann Combret, Margaux Machefert, Guillaume Prieur, Emeline Fresnel, Elise Artaud-Macari, Bouchra Lamia, Marius Lebret, Clément Medrinal
Publikováno v:
Intensive Care Medicine Experimental, Vol 12, Iss 1, Pp 1-8 (2024)
Abstract Purpose Tracheostomized patients often present with muscle weakness, altered consciousness, or swallowing difficulties. Hence, the literature is scarce regarding the challenging management of tracheostomy weaning. There is a need to strength
Externí odkaz:
https://doaj.org/article/a4aae0f113fe4dde9cb24cd2c3806899
Autor:
Manuela M. X. Tan, Michael A. Lawton, Miriam I. Pollard, Emmeline Brown, Raquel Real, Alejandro Martinez Carrasco, Samir Bekadar, Edwin Jabbari, Regina H. Reynolds, Hirotaka Iwaki, Cornelis Blauwendraat, Sofia Kanavou, Leon Hubbard, Naveed Malek, Katherine A. Grosset, Nin Bajaj, Roger A. Barker, David J. Burn, Catherine Bresner, Thomas Foltynie, Nicholas W. Wood, Caroline H. Williams-Gray, Ole A. Andreassen, Mathias Toft, Alexis Elbaz, Fanny Artaud, Alexis Brice, Jean-Christophe Corvol, Jan Aasly, Matthew J. Farrer, Michael A. Nalls, Andrew B. Singleton, Nigel M. Williams, Yoav Ben-Shlomo, John Hardy, Michele T. M. Hu, Donald G. Grosset, Maryam Shoai, Lasse Pihlstrøm, Huw R. Morris
Publikováno v:
npj Parkinson's Disease, Vol 10, Iss 1, Pp 1-15 (2024)
Abstract There are 90 independent genome-wide significant genetic risk variants for Parkinson’s disease (PD) but currently only five nominated loci for PD progression. The biology of PD progression is likely to be of central importance in defining
Externí odkaz:
https://doaj.org/article/b1dcf9f75c9e4bc4bc4699609e993749