Optimizing and extending the functionality of EXARL for scalable reinforcement learning

Autor:	Chenna, Sai, Cosburn, Katherine, Ezeobi, Uchenna, Moraru, Maxim, Lim, Hyun, Loiseau, Julien, Mohd-Yusof, Jamal, Pavel, Robert, Ramakrishnaiah, Vinay, Reisner, Andrew, Tsai, Karen
Přispěvatelé:	Los Alamos National Laboratory (LANL), University of Florida [Gainesville] (UF), The University of New Mexico [Albuquerque], University of Colorado [Colorado Springs] (UCCS), Université de Reims Champagne-Ardenne (URCA), Moraru, Maxim
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] [INFO.INFO-DC] Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC] [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] ACM: I.: Computing Methodologies/I.2: ARTIFICIAL INTELLIGENCE/I.2.11: Distributed Artificial Intelligence/I.2.11.3: Multiagent systems [INFO.INFO-DC]Computer Science [cs]/Distributed Parallel and Cluster Computing [cs.DC]
Zdroj:	SC21-Supercomputing 2021 SC21-Supercomputing 2021, Nov 2021, Saint-Louis, United States.
Popis:	International audience
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::7d9881fc2d361ef89c482956f8bbf7c0 https://cea.hal.science/cea-03780809/document Zobrazit plný text záznamu