Modular multi-agent reinforcement learning of linked multi-component robotic systems
- Manuel Graña Romay Zuzendaria
Defentsa unibertsitatea: Universidad del País Vasco - Euskal Herriko Unibertsitatea
Fecha de defensa: 2012(e)ko apirila-(a)k 23
- Ángel Pascual del Pobil Ferré Presidentea
- Francisco Xabier Albizuri Irigoyen Idazkaria
- Bruno Apolloni-Ghetti Kidea
- Michal Wozniak Kidea
- Richard J. Duro Fernández Kidea
Mota: Tesia
Laburpena
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.