Reinforcement learning and dynamic programming using function approximators /
General Material Designation
[Book]
First Statement of Responsibility
Lucian Buðsoniu [and others].
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
Boca Raton, FL :
Name of Publisher, Distributor, etc.
CRC Press,
Date of Publication, Distribution, etc.
[2010]
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
1 online resource (xiii, 270 pages) :
Other Physical Details
illustrations
SERIES
Series Title
Automation and control engineering
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
Text of Note
Includes bibliographical references and index.
CONTENTS NOTE
Text of Note
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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SUMMARY OR ABSTRACT
Text of Note
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.
ACQUISITION INFORMATION NOTE
Source for Acquisition/Subscription Address
Ingram Content Group
Stock Number
TANDF_211565
OTHER EDITION IN ANOTHER MEDIUM
Title
Reinforcement learning and dynamic programming using function approximators.