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210123 ||| eng |
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|a 1466549319
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|a 9781439856895
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|a 9781439856901
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|a 9780429105364
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|a 1439856893
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|a 9781466549319
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|a 1439856907
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4 |
|a Q325.6
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|a Sugiyama, Masashi
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|a Statistical reinforcement learning
|b modern machine learning approaches
|c Masashi Sugiyama
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|a Modern machine learning approaches
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| 260 |
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|a Boca Raton, FL
|b CRC Press
|c 2015
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| 300 |
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|a xiii, 189 pages
|b illustrations
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|a Includes bibliographical references (pages 183-189)
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|a Part IV: Model-Based Reinforcement LearningChapter 10: Transition Model Estimation; Chapter 11: Dimensionality Reduction for Transition Model Estimation; References
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|a Cover; Contents; Foreword; Preface; Author; Part I: Introduction; Chapter 1: Introduction to Reinforcement Learning; Part II: Model-Free Policy Iteration; Chapter 2: Policy Iteration with Value Function Approximation; Chapter 3: Basis Design for Value Function Approximation; Chapter 4: Sample Reuse in Policy Iteration; Chapter 5: Active Learning in Policy Iteration; Chapter 6: Robust Policy Iteration; Part III: Model-Free Policy Search; Chapter 7: Direct Policy Search by Gradient Ascent; Chapter 8: Direct Policy Search by Expectation-Maximization; Chapter 9: Policy-Prior Search
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| 653 |
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|a Machine learning / Mathematical models
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| 653 |
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|a Machine learning / Mathematical models / fast
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| 653 |
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|a Reinforcement learning / fast
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| 653 |
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|a Apprentissage par renforcement (Intelligence artificielle)
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| 653 |
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|a Apprentissage automatique / Modèles mathématiques
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| 653 |
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|a Reinforcement learning / http://id.loc.gov/authorities/subjects/sh92000704
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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|a Chapman & Hall/CRC machine learning & pattern recognition series
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| 776 |
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|z 0429105363
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| 776 |
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|z 9780429105364
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| 776 |
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|z 9781439856895
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|u https://learning.oreilly.com/library/view/~/9781439856895/?ar
|x Verlag
|3 Volltext
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|a 006.3/1
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|a 153.15
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|a Reinforcement learning is a mathematical framework for developing computer agents that can learn an optimal behavior by relating generic reward signals with its past actions. With numerous successful applications in business intelligence, plant control, and gaming, the RL framework is ideal for decision making in unknown environments with large amounts of data. Supplying an up-to-date and accessible introduction to the field, Statistical Reinforcement Learning: Modern Machine Learning Approaches presents fundamental concepts and practical algorithms of statistical reinforcement learning from th
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