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" Reinforcement learning and dynamic programming using function approximators / "
Lucian Buðsoniu [and others].
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BL
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| Record Number
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1031489
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| Doc. No
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b785859
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| Title & Author
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Reinforcement learning and dynamic programming using function approximators /\ Lucian Buðsoniu [and others].
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| Publication Statement
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Boca Raton, FL :: CRC Press,, [2010]
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| Series Statement
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Automation and control engineering
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| Page. NO
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1 online resource (xiii, 270 pages) :: illustrations
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| ISBN
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1439821097
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: 9781439821091
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1439821089
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9781439821084
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| Bibliographies/Indexes
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Includes bibliographical references and index.
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| Contents
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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.
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| Abstract
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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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Digital control systems.
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Dynamic programming.
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Digital control systems.
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Dynamic programming.
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TECHNOLOGY ENGINEERING-- Automation.
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| Dewey Classification
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629.8/9
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| LC Classification
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TJ223.M53R44 2010
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| Added Entry
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Busoniu, Lucian.
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