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...
Salvato in:
| Autore principale: | |
|---|---|
| Altri autori: | |
| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Edizione: | 1st ed. 2009. |
| Serie: | Studies in Computational Intelligence
216 |
| Accesso online: | 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 |
Sommario:
- Reinforcement Learning Background Related Work Empirical Domains Value Function Transfer via Inter-Task Mappings Extending Transfer via Inter-Task Mappings Transfer between Different Reinforcement Learning Methods Learning Inter-Task Mappings Conclusion and Future Work