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Título

Improving Reinforcement Learning by using Case-Based Heuristics

AutorBianchi, Reinaldo; Ros, Raquel; López de Mántaras, Ramón
Palabras claveCase Based Reasoning
CBR
Reinforcement Learning
Case Based Heuristically Accelerated Reinforcement Learning
Multiagent Learning
Fecha de publicación2009
EditorSpringer
CitaciónCase-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
ResumenThis 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.
DescripciónThe original publication is available at www.springerlink.com
Versión del editor10.1007/978-3-642-02998-1_7
URIhttp://hdl.handle.net/10261/18069
DOI10.1007/978-3-642-02998-1_7
ISBN978-3-642-02997-4
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