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Tutorial Linear Function Approximators by Geramifard Alborz (19 results)
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![Stock image for A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning: 20 (Foundations and Trends® in Machine Learning) [Paperback] Geramifard, Alborz; Walsh, Thomas J. and Tellex, Stefanie for sale by Hay-on-Wye Booksellers](https://pictures.abebooks.com/isbn/9781601987600-us-300.jpg)
A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning: 20 (Foundations and Trends® in Machine Learning) [Paperback] Geramifard, Alborz; Walsh, Thomas J. and Tellex, Stefanie
Seller: Hay-on-Wye Booksellers, Hay-on-Wye, HEREF, United Kingdom
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning (Foundations and Trends(r) in Machine Learning)
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Published by Now Publishers Inc 2013-12, 2013
ISBN 10: 1601987609 ISBN 13: 9781601987600
Language: English
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Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Published by now publishers Inc, US, 2013
ISBN 10: 1601987609 ISBN 13: 9781601987600
Language: English
Paperback. Condition: New.
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Published by now publishers Inc, US, 2013
ISBN 10: 1601987609 ISBN 13: 9781601987600
Language: English
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Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
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Add to basketPaperback. Condition: Brand New. 92 pages. 8.98x5.98x0.16 inches. In Stock.
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Add to basketCondition: New. Inhaltsverzeichnis1: Introduction 2: Dynamic Programming and Reinforcement Learning 3: Representations 4: Empirical Results 5: Summary. Acknowledgements. References.KlappentextA Markov Decision Process (.
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Published by now publishers Inc, US, 2013
ISBN 10: 1601987609 ISBN 13: 9781601987600
Language: English
US$ 95.64
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
Published by now publishers Inc, US, 2013
ISBN 10: 1601987609 ISBN 13: 9781601987600
Language: English
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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
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Add to basketPAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.
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A Tutorial on Linear Function Approximators for Dynamic Programming and Reinforcement Learning
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Add to basketPaperback / softback. Condition: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 142.
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Add to basketTaschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A Markov Decision Process (MDP) is a natural framework for formulating sequential decision-making problems under uncertainty. In recent years, researchers have greatly advanced algorithms for learning and acting in MDPs. This book reviews such algorithms, beginning with well-known dynamic programming methods for solving MDPs such as policy iteration and value iteration, then describes approximate dynamic programming methods such as trajectory based value iteration, and finally moves to reinforcement learning methods such as Q-Learning, SARSA, and least-squares policy iteration. It describes algorithms in a unified framework, giving pseudocode together with memory and iteration complexity analysis for each. Empirical evaluations of these techniques, with four representations across four domains, provide insight into how these algorithms perform with various feature sets in terms of running time and performance.This tutorial provides practical guidance for researchers seeking to extend DP and RL techniques to larger domains through linear value function approximation. The practical algorithms and empirical successes outlined also form a guide for practitioners trying to weigh computational costs, accuracy requirements, and representational concerns. Decision making in large domains will always be challenging, but with the tools presented here this challenge is not insurmountable.
