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ICCBR
2009
Springer

Improving Reinforcement Learning by Using Case Based Heuristics

11 years 8 months ago
Improving Reinforcement Learning by Using Case Based Heuristics
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.
Reinaldo A. C. Bianchi, Raquel Ros, Ramon Ló
Added 26 May 2010
Updated 26 May 2010
Type Conference
Year 2009
Where ICCBR
Authors Reinaldo A. C. Bianchi, Raquel Ros, Ramon López de Mántaras
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