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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning

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Statistical Reinforcement Learning - Modern Machine Learning Approaches (Hardcover) Loot Price: R2,674
Discovery Miles 26 740
Statistical Reinforcement Learning - Modern Machine Learning Approaches (Hardcover): Masashi Sugiyama

Statistical Reinforcement Learning - Modern Machine Learning Approaches (Hardcover)

Masashi Sugiyama

Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

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Loot Price R2,674 Discovery Miles 26 740 | Repayment Terms: R251 pm x 12*

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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 the modern machine learning viewpoint. It covers various types of RL approaches, including model-based and model-free approaches, policy iteration, and policy search methods. Covers the range of reinforcement learning algorithms from a modern perspective Lays out the associated optimization problems for each reinforcement learning scenario covered Provides thought-provoking statistical treatment of reinforcement learning algorithms The book covers approaches recently introduced in the data mining and machine learning fields to provide a systematic bridge between RL and data mining/machine learning researchers. It presents state-of-the-art results, including dimensionality reduction in RL and risk-sensitive RL. Numerous illustrative examples are included to help readers understand the intuition and usefulness of reinforcement learning techniques. This book is an ideal resource for graduate-level students in computer science and applied statistics programs, as well as researchers and engineers in related fields.

General

Imprint: Taylor & Francis
Country of origin: United States
Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
Release date: March 2015
First published: 2015
Authors: Masashi Sugiyama
Dimensions: 234 x 156 x 16mm (L x W x T)
Format: Hardcover
Pages: 206
ISBN-13: 978-1-4398-5689-5
Categories: Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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LSN: 1-4398-5689-3
Barcode: 9781439856895

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