Conference on Intelligent Autonomous Systems (IAS), 2005, Amsterdam (Países Bajos) ; The application of reinforcement learning techniques in the typically complex environments of the autonomous agents is only feasible when some kind of generalization among situations is possible in order to reduce the number of experiences required for the learning task. Many reinforcement learning techniques have been proposed to cope with this problem but they usually do not take advantage of all the opportunities of generalization. A new algorithm was proposed in [12] that exploits a type of regularity that is denoted as categorizability. Categorizability means that from all the relevant features that must be taken into account to decide the best action in any situation, only a few of them are actually relevant in each particular situation. In this paper the categorization and learning capabilities of the algorithm are evaluated using a problem which satisfies to a good extent the categorizability property. The categorization achieved by the algorithm in this problem is analysed in detail and illustrated with examples. The learning performance of the algorithm is compared with those of other reinforcement learning algorithms. Some improvements of the original algorithm are introduced. ; This work was supported by the project 'Sistema reconfigurable para la navegación basada en visión de robots caminantes y rodantes en entornos naturales.' (00). ; Peer Reviewed
Learning in complex environments with feature-based categorization
2005-01-01
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
DDC: | 629 |
Feature Selection for Image Categorization
British Library Conference Proceedings | 2006
|Feature Selection for Image Categorization
Springer Verlag | 2006
|Trajectory tracking control of a rotational joint using feature-based categorization learning
BASE | 2004
|