• Menu
  • Skip to right header navigation
  • Skip to main content
  • Skip to secondary navigation
  • Skip to primary sidebar

OnlineProgrammingBooks.com

Legally Free Computer Books

  • All Categories
  • All Books
  • All Categories
  • All Books
  • About Us
  • Privacy policy
  • Disclaimer
  • Subscribe
  • Contact
You are here: Home ▶ AI and Robotics ▶ Reinforcement Learning: An Introduction

Reinforcement Learning: An Introduction

March 24, 2006

Reinforcement Learning: An Introduction

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning.

Book Description

Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

Table of Contents

  • Introduction
  • Evaluative Feedback
  • The Reinforcement Learning Problem
  • Dynamic Programming
  • Monte Carlo Methods
  • Temporal-Difference Learning
  • Eligibility Traces
  • Generalization and Function Approximation
  • Planning and Learning
  • Dimensions of Reinforcement Learning
  • Case Studies

Download Free PDF / Read Online

Author(s): Richard S. Sutton and Andrew G. Barto.
Format(s): HTML
Number of pages: 322
Link: Read online.

Similar Books:

  1. Machine Learning
  2. Theory and Novel Applications of Machine Learning
  3. Robotics Automation and Control
  4. Compilers and Compiler Generators: An introduction with C++
  5. Project Management Guide – An Introduction to the Techniques
Previous Post: « Transport Phenomena
Next Post: Engineering Analysis »

Primary Sidebar

Get Latest Updates

  • Facebook
  • Pinterest
  • RSS
  • Twitter
  • YouTube
  • About Us
  • Privacy policy
  • Disclaimer
  • Subscribe
  • Contact

Copyright © 2006–2023 OnlineProgrammingBooks.com