Modular multi-agent reinforcement learning of linked multi-component robotic systems

  1. FERNANDEZ GAUNA, BORJA
unter der Leitung von:
  1. Manuel Graña Romay Doktorvater/Doktormutter

Universität der Verteidigung: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 23 von April von 2012

Gericht:
  1. Ángel Pascual del Pobil Ferré Präsident/in
  2. Francisco Xabier Albizuri Irigoyen Sekretär/in
  3. Bruno Apolloni-Ghetti Vocal
  4. Michal Wozniak Vocal
  5. Richard J. Duro Fernández Vocal
Fachbereiche:
  1. Ciencia de la Computación e Inteligencia Artificial

Art: Dissertation

Teseo: 115276 DIALNET

Zusammenfassung

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.