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Reinforcement learning and dynamic programming using function approximators /
From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dyn...
| Clasificación: | Libro Electrónico |
|---|---|
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2010]
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| Colección: | Automation and control engineering.
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 245 | 0 | 0 | |a Reinforcement learning and dynamic programming using function approximators / |c Lucian Buðsoniu [and others]. |
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c [2010] | |
| 300 | |a 1 online resource (xiii, 270 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 338 | |a online resource |b cr |2 rdacarrier | ||
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| 490 | 1 | |a Automation and control engineering | |
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a ch. 1. Introduction -- ch. 2. An introduction to dynamic programming and reinforcement learning -- ch. 3. Dynamic programming and reinforcement learning in large and continuous spaces -- ch. 4. Approximate value iteration with a fuzzy representation -- ch. 5. Approximate policy iteration for online learning and continuous-action control -- ch. 6. Approximate policy search with cross-entropy optimization of basis functions. | |
| 588 | 0 | |a Print version record. | |
| 520 | |a From household appliances to applications in robotics, engineered systems involving complex dynamics can only be as effective as the algorithms that control them. While Dynamic Programming (DP) has provided researchers with a way to optimally solve decision and control problems involving complex dynamic systems, its practical value was limited by algorithms that lacked the capacity to scale up to realistic problems. However, in recent years, dramatic developments in Reinforcement Learning (RL), the model-free counterpart of DP, changed our understanding of what is possible. Those dev. | ||
| 590 | |a ProQuest Ebook Central |b Ebook Central Academic Complete | ||
| 650 | 0 | |a Digital control systems. | |
| 650 | 0 | |a Dynamic programming. | |
| 650 | 6 | |a Commande numérique. | |
| 650 | 6 | |a Programmation dynamique. | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Automation. |2 bisacsh | |
| 650 | 7 | |a Digital control systems |2 fast | |
| 650 | 7 | |a Dynamic programming |2 fast | |
| 700 | 1 | |a Busoniu, Lucian. | |
| 758 | |i has work: |a Reinforcement learning and dynamic programming using function approximators (Work) |1 https://id.oclc.org/worldcat/entity/E39PD3yHPq6QtxppxP86x7k8P3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |t Reinforcement learning and dynamic programming using function approximators. |d Boca Raton, FL : CRC Press, ©2010 |z 9781439821084 |w (DLC) 2010010207 |w (OCoLC)406174370 |
| 830 | 0 | |a Automation and control engineering. | |
| 856 | 4 | 0 | |u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=589872 |z Texto completo |
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