|Títol||Improving Reinforcement Learning by using Case-Based Heuristics|
|Publication Type||Conference Paper|
|Year of Publication||2009|
|Authors||Bianchi R, Ros R, de Mántaras RLópez|
|Conference Name||ICCBR'09: 8th International Conference on Case-Based Reasoning|
|Editor||Lecture Notes in Artificial Intelligence, Springer|
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.
- Quant a IIIA