Modular multi-agent reinforcement learning of linked multi-component robotic systems
- Manuel Graña Romay Director
Defence university: Universidad del País Vasco - Euskal Herriko Unibertsitatea
Fecha de defensa: 23 April 2012
- Ángel Pascual del Pobil Ferré Chair
- Francisco Xabier Albizuri Irigoyen Secretary
- Bruno Apolloni-Ghetti Committee member
- Michal Wozniak Committee member
- Richard J. Duro Fernández Committee member
Type: Thesis
Abstract
THE CONTENTS OF THIS THESIS CAN BE SUMMARIZED AS TWO MAIN IDEAS: MODULAR TECHNIQUES TO DECOMPOSE A REINFORCEMENT LEARNING TASK IN OVER-CONSTRAINED ENVIRONMENTS SUCH AS LINKED-MCRS AS SEVERAL CONCURRENT SUB-TASKS, AND EXTENSION OF THESE MODULAR REINFORCEMENT LEARNING APPROACHES TO MULTI-AGENT REINFORCEMENT LEARNING ENVIRONMENTS.