Cargando…
Sample-efficient multi-agent reinforcement learning with masked reconstruction
Deep reinforcement learning (DRL) is a powerful approach that combines reinforcement learning (RL) and deep learning to address complex decision-making problems in high-dimensional environments. Although DRL has been remarkably successful, its low sample efficiency necessitates extensive training ti...
Autores principales: | Kim, Jung In, Lee, Young Jae, Heo, Jongkook, Park, Jinhyeok, Kim, Jaehoon, Lim, Sae Rin, Jeong, Jinyong, Kim, Seoung Bum |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501567/ https://www.ncbi.nlm.nih.gov/pubmed/37708154 http://dx.doi.org/10.1371/journal.pone.0291545 |
Ejemplares similares
-
Multi-agent reinforcement learning with approximate model learning for competitive games
por: Park, Young Joon, et al.
Publicado: (2019) -
A Machine Learning-Based Study of the Effects of Air Pollution and Weather in Respiratory Disease Patients Visiting Emergency Departments
por: Lee, Eu Sun, et al.
Publicado: (2022) -
Quantitative Evaluation of the Dispersion of Graphene Sheets With and Without Functional Groups Using Molecular Dynamics Simulations
por: Cha, JinHyeok, et al.
Publicado: (2016) -
Feature Selection Method Using Multi-Agent Reinforcement Learning Based on Guide Agents
por: Kim, Minwoo, et al.
Publicado: (2022) -
Submental Intubation with Reinforced Tube for Intubating Laryngeal Mask Airway
por: Kim, Ki Jun, et al.
Publicado: (2005)