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Improving reinforcement learning with interactive feedback and affordances

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20 Citas (Scopus)

Resumen

Interactive reinforcement learning constitutes an alternative for improving convergence speed in reinforcement learning methods. In this work, we investigate inter-agent training and present an approach for knowledge transfer in a domestic scenario where a first agent is trained by reinforcement learning and afterwards transfers selected knowledge to a second agent by instructions to achieve more efficient training. We combine this approach with action-space pruning by using knowledge on affordances and show that it significantly improves convergence speed in both classic and interactive reinforcement learning scenarios.

Idioma originalInglés
Título de la publicación alojadaIEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas165-170
Número de páginas6
ISBN (versión digital)9781479975402
DOI
EstadoPublicada - 11 dic. 2014
Publicado de forma externa
Evento4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014 - Genoa, Italia
Duración: 13 oct. 201416 oct. 2014

Serie de la publicación

NombreIEEE ICDL-EPIROB 2014 - 4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics

Conferencia

Conferencia4th Joint IEEE International Conference on Development and Learning and on Epigenetic Robotics, IEEE ICDL-EPIROB 2014
País/TerritorioItalia
CiudadGenoa
Período13/10/1416/10/14

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