Exploiting Environment Configurability in Reinforcement Learning
In recent decades, Reinforcement Learning (RL) has emerged as an effective approach to address complex control tasks. In a Markov Decision Process (MDP), the framework typically used, the environment is assumed to be a fixed entity that cannot be altered externally. There are, however, several real-...
Uloženo v:
| Hlavní autor: | |
|---|---|
| Médium: | Livre numérique |
| Jazyk: | Anglais |
| Vydáno: |
London :
SAGE Publications
2022.
Paris : Cyberlibris |
| On-line přístup: | Accès Université d'Orléans et IFPM |
| Poznámka: |
Couverture. https://static2.cyberlibris.com/books_upload/300pix/9781643683638.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Exploiting Environment Configurability in Reinforcement Learning, Alberto Maria Metelli, London, SAGE Publications, 2022, 1 vol. (377 p.), 978-16-4368-362-1 |