Statistical reinforcement learning modern machine learning approaches
Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for deci...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, Florida :
CRC Press
[2015]
|
| Edición: | 1st edition |
| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009628944706719 |
| Sumario: | Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data.Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from th |
|---|---|
| Notas: | A Chapman and Hall book. |
| Descripción Física: | 1 online resource (206 p.) |
| Bibliografía: | Includes bibliographical references. |
| ISBN: | 9781040058152 9780429105364 9781439856895 9781439856901 |
