Statistical reinforcement learning : modern machine learning approaches /

Bibliographic Details
Main Author:	Sugiyama, Masashi, 1974- (Author)
Corporate Author:	Taylor & Francis
Format:	eBook
Language:	English
Published:	Boca Raton, FL : CRC Press, [2015]
Series:	Chapman & Hall/CRC machine learning & pattern recognition series.
Subjects:	Reinforcement learning. Machine learning > Mathematical models. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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.
Part IV: Model-Based Reinforcement LearningChapter 10: Transition Model Estimation; Chapter 11: Dimensionality Reduction for Transition Model Estimation; References.