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Improving reinforcement learning by using case based heuristics

  • Reinaldo A.C. Bianchi
  • , Raquel Ros
  • , Ramon Lopez De Mantaras

Producción científica: Capítulo del libroContribución a congreso/conferenciarevisión exhaustiva

35 Citas (Scopus)

Resumen

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HARL is a subset of RL that makes use of a heuristic function derived from a case base, in a Case Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Q-Learning is also proposed. Empirical evaluations were conducted in a simulator for the RoboCup Four-Legged Soccer Competition, and results obtained shows that using CB-HARL, the agents learn faster than using either RL or HARL methods.

Idioma originalInglés
Título de la publicación alojadaCase-Based Reasoning Research and Development - 8th International Conference on Case-Based Reasoning, ICCBR 2009, Proceedings
Páginas75-89
Número de páginas15
DOI
EstadoPublicada - 2009
Publicado de forma externa
Evento8th International Conference on Case-Based Reasoning, ICCBR 2009 - Seattle, WA, Estados Unidos
Duración: 20 jul 200923 jul 2009

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen5650 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia8th International Conference on Case-Based Reasoning, ICCBR 2009
País/TerritorioEstados Unidos
CiudadSeattle, WA
Período20/07/0923/07/09

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