| Full text | |
| Author(s): |
Bianchi, Reinaldo A. C.
;
Ros, Raquel
;
Lopez de Mantaras, Ramon
;
McGinty, L
;
Wilson, DC
Total Authors: 5
|
| Document type: | Journal article |
| Source: | Lecture Notes in Computer Science; v. 5650, p. 3-pg., 2009-01-01. |
| Abstract | |
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. (AU) | |
| FAPESP's process: | 09/01610-1 - Development of control strategies based on intelligence of swarms applied to Football Robots |
| Grantee: | Danilo Tadashi Doi |
| Support Opportunities: | Scholarships in Brazil - Scientific Initiation |