Reinforcement Learning and Dynamic Programming Using Function Approximators
While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on conti...
Uloženo v:
| Hlavní autoři: | , , , |
|---|---|
| Médium: | E-kniha Kniha |
| Jazyk: | angličtina |
| Vydáno: |
Boca Raton
CRC Press
2010
Taylor & Francis Group |
| Vydání: | 1 |
| Edice: | Automation and control engineering |
| Témata: | |
| ISBN: | 1439821089, 9781439821084 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Shrnutí: | While Dynamic Programming (DP) has helped solve control problems involving dynamic systems, its value was limited by algorithms that lacked practical scale-up capacity. In recent years, developments in Reinforcement Learning (RL), DP's model-free counterpart, has changed this. Focusing on continuous-variable problems, this unparalleled work provides an introduction to classical RL and DP, followed by a presentation of current methods in RL and DP with approximation. Combining algorithm development with theoretical guarantees, it offers illustrative examples that readers will be able to adapt to their own work. |
|---|---|
| Bibliografie: | Includes bibliographical references and index scopus-id:2-s2.0-85046476577 Automation & control engineering |
| ISBN: | 1439821089 9781439821084 |
| DOI: | 10.1201/9781439821091 |