Reinaldo BianchiROS, R.LOPES, DE M. R.2022-01-122022-01-122009-07-23BIANCHI, R.; ROS, R.; LOPES, DE M. R. Improving reinforcement learning by using case based heuristics. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). p. 75-89, July, 2009.0302-9743https://repositorio.fei.edu.br/handle/FEI/4281This 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. © 2009 Springer Berlin Heidelberg.Acesso RestritoArtigo de evento10.1007/978-3-642-02998-1_7