Transfer in reinforcement learning domains
In reinforcement learning (RL) problems, learning agents sequentially execute actions with the goal of maximizing a reward signal. The RL framework has gained popularity with the development of algorithms capable of mastering increasingly complex problems, but learning difficult tasks is often slow...
Gardado en:
| Autor Principal: | |
|---|---|
| Outros autores: | |
| Formato: | Livre numérique |
| Idioma: | Anglais |
| Publicado: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edición: | 1st ed. 2009. |
| Series: | Studies in Computational Intelligence
216 |
| Acceso en liña: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Transfer in reinforcement learning domains, Texte imprimé, 9783642018817 • Transfer in Reinforcement Learning Domains, Texte imprimé, 9783642018831 • Transfer in Reinforcement Learning Domains, Texte imprimé, 9783642018817 • Transfer in Reinforcement Learning Domains, Texte imprimé, 9783642101861 |