Por favor, use este identificador para citar o enlazar a este item:
http://hdl.handle.net/10261/30202
COMPARTIR / EXPORTAR:
SHARE BASE | |
Visualizar otros formatos: MARC | Dublin Core | RDF | ORE | MODS | METS | DIDL | DATACITE | |
Título: | Reinforcement learning and automatic categorization |
Autor: | Porta, Josep M. CSIC ORCID ; Celaya, Enric CSIC ORCID | Palabras clave: | Automation: Robots Robots Robotics |
Fecha de publicación: | 1999 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | 7th IEEE International Conference on Emerging Technologies and Factory Automation: 159-166 (1999) | Resumen: | The categorization process defines sensor and action categories from elementary sensor readings and basic actions so that the necessary elements for solving a task are correctly perceived and manipulated. In reinforcement learning, a previous categorization process is needed to define sensor and action categories with special requirements that we analyze and that, in general, are difficult to achieve, especially in complex tasks such as those that arise when working with autonomous robots. We show how these special requirements should be relaxed and we sketch a reinforcement learning algorithm that uses a less restrictive form of sensory categorization than existing algorithms. Additionally, we show how a given sensory categorization can be improved so that it better fits the demands of the previous algorithm. | Descripción: | IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 1999, Barcelona (España) | URI: | http://hdl.handle.net/10261/30202 | DOI: | 10.1109/ETFA.1999.815351 | ISBN: | 780356705 |
Aparece en las colecciones: | (IRII) Comunicaciones congresos |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | |
---|---|---|---|---|
doc1.pdf | 832,13 kB | Adobe PDF | Visualizar/Abrir |
CORE Recommender
Page view(s)
303
checked on 23-abr-2024
Download(s)
195
checked on 23-abr-2024
Google ScholarTM
Check
Altmetric
Altmetric
NOTA: Los ítems de Digital.CSIC están protegidos por copyright, con todos los derechos reservados, a menos que se indique lo contrario.