Reinforcement Learning and Dynamic Programming Using Function Approximators - Robert Babuska,Lucian Busoniu,Bart De Schutter
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Toimitus 17-23 arkipäivässä
30 päivän palautusoikeus
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 pres ... Täydellinen kuvaus
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Kuvaus
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.
Lisätietoja
| Kirjoittaja | Robert Babuska, Lucian Busoniu, Bart De Schutter |
|---|---|
| Julkaisija | CRC Press |
| Julkaisuvuosi | 2010 |
| Kannen tyyppi | Kovakantinen |
| EAN | 9781439821084 |