| Título |
Supervised reinforcement learning in discrete environment domains |
| Tipo |
Congreso |
| Sub-tipo |
SCOPUS |
| Descripción |
2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010 |
| Resumen |
This paper describes a supervised reinforcement learning-based model for discrete environment domains. The model was tested within the domain of backgammon game. Our results show that a supervised actor-critic based learning model is capable of improving the initial performance and then eventually reach similar performance levels as those obtained by TD-Gammon, an artificial neural network player (ANN) trained by temporal differences. |
| Observaciones |
2010 2nd World Congress on Nature and Biologically Inspired Computing, NaBIC 2010; Category numberCFP1095H-ART; Code 84197 |
| Lugar |
Kitakyushu |
| País |
Japon |
| No. de páginas |
215-220 |
| Vol. / Cap. |
Article number 5716276 |
| Inicio |
2010-12-15 |
| Fin |
2010-12-17 |
| ISBN/ISSN |
978-142447376-2 |
