Statistical Reinforcement Learning : Modern Machine Learning Approaches

By:

Sugiyama, Masashi

Material type: Text

TextPublication details: New York CRC Press 2024Description: xiv, 192p. 23 x 15 cmISBN:

9781032708119

DDC classification:

006.31 SUG

Summary: I. Introduction 1. Introduction to Reinforcement Learning II. Model-Free Policy Iteration 2. Policy Iteration with Value Function Approximation 3. Basis Design for Value Function Approximation 4. Sample Reuse in Policy Iteration 5. Active Learning in Policy Iteration 6. Robust Policy Iteration III. Model-Free Policy Search 7. Direct Policy Search by Gradient Ascent 8. Direct Policy Search by Expectation-Maximization 9. Policy-Prior Search IV. Model-Based Reinforcement Learning 10. Transition Model Estimation Dimensionality Reduction for Transition Model Estimation

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Item type	Current library	Collection	Call number	Status	Date due	Barcode
Reference	Raj Kumar Goel Institute of Technology	DS (Data Science)	006.31 SUG (Browse shelf(Opens below))	Not for loan		96314
Reference	Raj Kumar Goel Institute of Technology	DS (Data Science)	006.31 SUG (Browse shelf(Opens below))	Not for loan		96315

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I. Introduction

1. Introduction to Reinforcement Learning

II. Model-Free Policy Iteration

2. Policy Iteration with Value Function Approximation

3. Basis Design for Value Function Approximation

4. Sample Reuse in Policy Iteration

5. Active Learning in Policy Iteration

6. Robust Policy Iteration

III. Model-Free Policy Search

7. Direct Policy Search by Gradient Ascent

8. Direct Policy Search by Expectation-Maximization

9. Policy-Prior Search

IV. Model-Based Reinforcement Learning

10. Transition Model Estimation

Dimensionality Reduction for Transition Model Estimation

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Statistical Reinforcement Learning : Modern Machine Learning Approaches

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