Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Arquímides Méndez-Molina"'
Autor:
Arquímides Méndez-Molina
Publikováno v:
IJCAI
The relation between Reinforcement learning (RL) and Causal Modeling(CM) is an underexplored area with untapped potential for any learning task. In this extended abstract of our Ph.D. research proposal, we present a way to combine both areas to impro
Publikováno v:
Advances in Computational Intelligence ISBN: 9783030898168
MICAI (1)
MICAI (1)
Reinforcement learning (RL) is the de facto learning by interaction paradigm within machine learning. One of the intrinsic challenges of RL is the trade-off between exploration and exploitation. To solve this problem, in this paper, we propose to imp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7ff3ac5ca07ceb8ea23b609a83015046
https://doi.org/10.1007/978-3-030-89817-5_16
https://doi.org/10.1007/978-3-030-89817-5_16
Autor:
Ana Li Oña-García, José Fco. Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, Arquímides Méndez-Molina
Publikováno v:
Journal of Intelligent & Fuzzy Systems. 34:2949-2957
Autor:
Sebastián Salazar-Colores, Arquímides Méndez-Molina, Esaú Escobar-Juárez, David Carrillo-López, Eduardo F. Morales, L. Enrique Sucar
Publikováno v:
Advances in Soft Computing ISBN: 9783030337483
MICAI
MICAI
One of the most important skills for service robots is object manipulation, which is still a challenging task. Since object manipulation is a hard task, it is relevant to know if an object was successfully grasped, avoiding future wrong decisions. Ob
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::084a6cd47c42614620a1c44eb648cd6d
https://doi.org/10.1007/978-3-030-33749-0_48
https://doi.org/10.1007/978-3-030-33749-0_48
Learning robotic manipulation tasks using relational reinforcement learning and human demonstrations
Autor:
Arquímides Méndez Molina
Publikováno v:
Instituto Nacional de Astrofísica, Óptica y Electrónica
INAOE
Repositorio Institucional del INAOE
INAOE
Repositorio Institucional del INAOE
Autonomy in robots depends to a large extent on their ability to learn to perform new tasks as they are required. The current techniques for learning tasks strongly depend on information given by expert users and often, for a robot, what is learned i
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3056::b4cdde5317829caafb7d885ff78c3dd8
http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1577
http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/1577