Author(s) Name:  Masashi Sugiyama

About the Book:

   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.

Table of Contents

Model-Free Policy Iteration

  Policy Iteration with Value Function Approximation
  Basis Design for Value Function Approximation
  Sample Reuse in Policy Iteration
  Active Learning in Policy Iteration
  Robust Policy Iteration

Model-Free Policy Search

  Direct Policy Search by Gradient Ascent
  Direct Policy Search by Expectation-Maximization
  Policy-Prior Search

Model-Based Reinforcement Learning

  Transition Model Estimation
  Dimensionality Reduction for Transition Model Estimation

ISBN:  9780367575861

Publisher:  Chapman and Hall/CRC Publisher

Year of Publication:  2015

Book Link:  Home Page Url