Reinforcement Learning

Author: Richard S. Sutton
Publisher: MIT Press
ISBN: 9780262193986
Size: 18.45 MB
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The discussion ranges from the history of the field's intellectual foundations to recent developments and applications. Areas studied include reinforcement learning problems in terms of Markov decision problems and solution methods.

Reinforcement Learning

Author: Richard S. Sutton
Publisher: Springer Science & Business Media
ISBN: 1461536189
Size: 61.80 MB
Format: PDF, Docs
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Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work.

Reinforcement Learning

Author: Richard S. Sutton
Publisher: Springer Science & Business Media
ISBN: 9780792392347
Size: 15.16 MB
Format: PDF, Mobi
View: 6445
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Of course learning and reinforcement have been studied in psychology for almost a century, and that work has had a very strong impact on the AI/engineering work.

Introduction To Machine Learning

Author: Ethem Alpaydin
Publisher: MIT Press
ISBN: 0262028182
Size: 35.72 MB
Format: PDF, ePub
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Introduction -- Supervised learning -- Bayesian decision theory -- Parametric methods -- Multivariate methods -- Dimensionality reduction -- Clustering -- Nonparametric methods -- Decision trees -- Linear discrimination -- Multilayer ...

Algorithms For Reinforcement Learning

Author: Csaba Szepesvari
Publisher: Morgan & Claypool Publishers
ISBN: 1608454924
Size: 58.86 MB
Format: PDF, ePub
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In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming.We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state ...

Introduction To Statistical Relational Learning

Author: Lise Getoor
Publisher: MIT Press
ISBN: 0262072882
Size: 20.16 MB
Format: PDF, Kindle
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The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, ...

Learning In Graphical Models

Author: M.I. Jordan
Publisher: Springer Science & Business Media
ISBN: 9401150141
Size: 37.63 MB
Format: PDF, Mobi
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The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail.

Reinforcement Learning

Author: Marco Wiering
Publisher: Springer Science & Business Media
ISBN: 3642276458
Size: 52.87 MB
Format: PDF
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As a field, reinforcement learning has progressed tremendously in the past decade. The main goal of this book is to present an up-to-date series of survey articles on the main contemporary sub-fields of reinforcement learning.

Probabilistic Graphical Models

Author: Daphne Koller
Publisher: MIT Press
ISBN: 0262013193
Size: 60.58 MB
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Proceedings of the annual Conference on Uncertainty in Artificial Intelligence, available for 1991-present.