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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 |
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| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2010]
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| Colección: | Automation and control engineering.
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| Temas: | |
| Acceso en línea: | Texto completo |
| Sumario: | 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. |
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| Descripción Física: | 1 online resource (xiii, 270 pages) : illustrations |
| Bibliografía: | Includes bibliographical references and index. |
| ISBN: | 9781439821091 1439821097 9781315217932 1315217937 |