Statistical Reinforcement Learning: Modern Machine Learning Approaches

✍ Scribed by Sugiyama, Masashi

• Publisher: CRC Press
• Year: 2015
• Tongue: English
• Leaves: 206
• Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition
• Category: Library

✦ Synopsis

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 th. Read more...

Abstract: 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 th

✦ Table of Contents

Content: 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