Título
Relational reinforcement learning with continuous actions by combining behavioural cloning and locally weighted regression
Autor
EDUARDO FRANCISCO MORALES MANZANARES
Nivel de Acceso
Acceso Abierto
Materias
Relational Reinforcement Learning - (RELATIONAL REINFORCEMENT LEARNING) Behavioural Cloning - (BEHAVIOURAL CLONING) Continuous Actions - (CONTINUOUS ACTIONS) Robotics - (ROBOTICS) CIENCIAS FÍSICO MATEMÁTICAS Y CIENCIAS DE LA TIERRA - (CTI) MATEMÁTICAS - (CTI) CIENCIA DE LOS ORDENADORES - (CTI) CIENCIA DE LOS ORDENADORES - (CTI)
Resumen o descripción
Reinforcement Learning is a commonly used technique for learning tasks in robotics, however, traditional algorithms are unable to handle large amounts of data coming from the robot’s sensors, require long training times, and use dis-crete actions. This work introduces TS-RRLCA, a two stage method to tackle these problems. In the first stage, low-level data coming from the robot’s sensors is transformed into a more natural, relational representation based on rooms, walls, corners, doors and obstacles, significantly reducing the state space. We use this representation along with Be-havioural Cloning, i.e., traces provided by the user; to learn, in few iterations, a relational control policy with discrete actions which can be re-used in different environments. In the second stage, we use Locally Weighted Regression to transform the initial policy into a continuous actions policy. We tested our approach in simulation and with a real ser-vice robot on different environments for different navigation and following tasks. Results show how the policies can be used on different domains and perform smoother, faster and shorter paths than the original discrete actions policies.
Editor
Scientific Research
Fecha de publicación
2010
Tipo de publicación
Artículo
Versión de la publicación
Versión aceptada
Recurso de información
Formato
application/pdf
Idioma
Inglés
Relación
&
Morales-Manzanares, E.F. (2010). Relational reinforcement learning with continuous actions by combining behavioural cloning and locally weighted regression, J. Intelligent Learning Systems
&
Applications, (2): 69-79
Audiencia
Estudiantes
Investigadores
Público en general
Sugerencia de citación
Zaragoza, J.H.
Repositorio Orígen
Repositorio Institucional del INAOE
Descargas
230