Learning in real robots from environment interaction
- Quintía Vidal, Pablo
- Iglesias, P.
- Rodríguez González, Miguel Ángel
- Regueiro, Carlos V.
- Valdés Villarrubia, Fernando
ISSN: 1888-0258
Año de publicación: 2012
Título del ejemplar: Advances on physical agents
Volumen: 6
Número: 1
Páginas: 6
Tipo: Artículo
Otras publicaciones en: JoPha: Journal of Physical Agents
Resumen
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.