Modular multi-agent reinforcement learning of linked multi-component robotic systems
- Manuel Graña Romay Directeur
Université de défendre: Universidad del País Vasco - Euskal Herriko Unibertsitatea
Fecha de defensa: 23 avril 2012
- Ángel Pascual del Pobil Ferré President
- Francisco Xabier Albizuri Irigoyen Secrétaire
- Bruno Apolloni-Ghetti Rapporteur
- Michal Wozniak Rapporteur
- Richard J. Duro Fernández Rapporteur
Type: Thèses
Résumé
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.