Adaptive Representations for Reinforcement Learning
This book presents new algorithms for reinforcement learning, a form of machine learning in which an autonomous agent seeks a control policy for a sequential decision task. Since current methods typically rely on manually designed solution representations, agents that automatically adapt their own r...
Enregistré dans:
| Hovedforfatter: | |
|---|---|
| Format: | Livre numérique |
| Sprog: | Anglais |
| Udgivet: |
Berlin, Heidelberg :
Springer Berlin Heidelberg
[20..].
Cham : Springer Nature |
| Udgivelse: | 1st ed. 2010. |
| Serier: | Studies in Computational Intelligence
291 |
| Online adgang: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Kommentar: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Adaptive Representations for Reinforcement Learning, Texte imprimé, 9783642139314 • Adaptive Representations for Reinforcement Learning, Texte imprimé, 9783642422317 • Adaptive Representations for Reinforcement Learning, Texte imprimé, 9783642139314 • Adaptive Representations for Reinforcement Learning, Texte imprimé, 9783642139338 |
Indholdsfortegnelse:
- Part 1 Introduction Part 2 Reinforcement Learning Part 3 On-Line Evolutionary Computation Part 4 Evolutionary Function Approximation Part 5 Sample-Efficient Evolutionary Function Approximation Part 6 Automatic Feature Selection for Reinforcement Learning Part 7 Adaptive Tile Coding Part 8 RelatedWork Part 9 Conclusion Part 10 Statistical Significance