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http://hdl.handle.net/10261/18069
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Título: | Improving Reinforcement Learning by using Case-Based Heuristics |
Autor: | Bianchi, Reinaldo; Ros, Raquel; Lopez de Mantaras, Ramon |
Palabras clave: | Case Based Reasoning CBR Reinforcement Learning Case Based Heuristically Accelerated Reinforcement Learning Multiagent Learning |
Fecha de publicación: | 2009 |
Editor: | Springer |
Citación: | Case-Based Reasoning Research and Development, 8th International Conference on Case-Based Reasoning, ICCBR 2009 Seattle, WA, USA, July 20-23, 2009 Proceedings. Lecture Notes in Artificial Intelligence, Vol. 5650, p.p.: 75-89, Springer Verlag, 2009 |
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. |
Descripción: | The original publication is available at www.springerlink.com |
Versión del editor: | 10.1007/978-3-642-02998-1_7 |
URI: | http://hdl.handle.net/10261/18069 |
DOI: | 10.1007/978-3-642-02998-1_7 |
ISBN: | 978-3-642-02997-4 |
Aparece en las colecciones: | (IIIA) Comunicaciones congresos |
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