Skip to main content
Taylor & Francis Group Logo
    Advanced Search

    Click here to search products using title name,author name and keywords.

    • Login
    • Hi, User  
      • Your Account
      • Logout
      Advanced Search

      Click here to search products using title name,author name and keywords.

      Breadcrumbs Section. Click here to navigate to respective pages.

      Book

      Statistical Reinforcement Learning
      loading

      Book

      Statistical Reinforcement Learning

      DOI link for Statistical Reinforcement Learning

      Statistical Reinforcement Learning book

      Modern Machine Learning Approaches

      Statistical Reinforcement Learning

      DOI link for Statistical Reinforcement Learning

      Statistical Reinforcement Learning book

      Modern Machine Learning Approaches
      ByMasashi Sugiyama
      Edition 1st Edition
      First Published 2015
      eBook Published 12 March 2015
      Pub. Location New York
      Imprint Chapman and Hall/CRC
      DOI https://doi.org/10.1201/b18188
      Pages 206
      eBook ISBN 9780429105364
      Subjects Computer Science, Mathematics & Statistics
      Share
      Share

      Get Citation

      Sugiyama, M. (2015). Statistical Reinforcement Learning: Modern Machine Learning Approaches (1st ed.). Chapman and Hall/CRC. https://doi.org/10.1201/b18188

      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 amo

      TABLE OF CONTENTS

      part 1|2 pages

      Part I: Introduction

      chapter 1|12 pages

      Introduction to Reinforcement Learning

      part 2|2 pages

      Part II: Model-Free Policy Iteration

      chapter 2|10 pages

      Policy Iteration with Value Function Approximation

      chapter 3|20 pages

      Basis Design for Value Function Approximation

      chapter 4|18 pages

      Sample Reuse in Policy Iteration

      chapter 5|14 pages

      Active Learning in Policy Iteration

      chapter 6|14 pages

      Robust Policy Iteration

      part 3|2 pages

      Part III: Model-Free Policy Search

      chapter 7|22 pages

      Direct Policy Search by Gradient Ascent

      chapter 8|16 pages

      Direct Policy Search by Expectation-Maximization

      chapter 9|22 pages

      Policy-Prior Search

      part 4|2 pages

      Part IV: Model-Based Reinforcement Learning

      chapter 10|16 pages

      Transition Model Estimation

      chapter 11|10 pages

      Dimensionality Reduction for Transition Model Estimation

      T&F logoTaylor & Francis Group logo
      • Policies
        • Privacy Policy
        • Terms & Conditions
        • Cookie Policy
        • Privacy Policy
        • Terms & Conditions
        • Cookie Policy
      • Journals
        • Taylor & Francis Online
        • CogentOA
        • Taylor & Francis Online
        • CogentOA
      • Corporate
        • Taylor & Francis Group
        • Taylor & Francis Group
        • Taylor & Francis Group
        • Taylor & Francis Group
      • Help & Contact
        • Students/Researchers
        • Librarians/Institutions
        • Students/Researchers
        • Librarians/Institutions
      • Connect with us

      Connect with us

      Registered in England & Wales No. 3099067
      5 Howick Place | London | SW1P 1WG © 2022 Informa UK Limited