Título: Learning in real robots from environment interaction
Autores: Quintía Vidal, Pablo
Iglesias Rodríguez, Roberto
Rodríguez González, Miguel Ángel
Vázquez Regueiro, Carlos
Valdés Villarrubia, Fernando
Fecha: 2012-03-15
2012-03-15
2012-01
Publicador: RUA Docencia
Fuente:
Tipo: info:eu-repo/semantics/article
Tema: Continuous robot learning
Robot adaptation
Learning from environment interaction
Reinforcement learning
Ciencia de la Computación e Inteligencia Artificial
Descripción: This article describes a proposal to achieve fast robot learning from its interaction with the environment. Our proposal will be suitable for continuous learning procedures as it tries to limit the instability that appears every time the robot encounters a new situation it had not seen before. On the other hand, the user will not have to establish a degree of exploration (usual in reinforcement learning) and that would prevent continual learning procedures. Our proposal will use an ensemble of learners able to combine dynamic programming and reinforcement learning to predict when a robot will make a mistake. This information will be used to dynamically evolve a set of control policies that determine the robot actions.
This work was supported by the research grants TIN2009-07737 and INCITE08PXIB262202PR.
Idioma: Inglés

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