Reinforcement Learning : An Introduction / Richard S. Sutton and Andrew G. Barto.

By: Contributor(s): Material type: TextTextSeries: Adaptive computation and machine learning | Book collections on Project MUSEPublisher: Cambridge, Mass. : MIT Press, 1998Manufacturer: Baltimore, Md. : Project MUSE, 2018Copyright date: ©1998Description: 1 online resource: illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780262257053
Subject(s): Genre/Form: Online resources:
Contents:
Contents -- Series Foreword -- Preface -- I. The Problem -- 1. Introduction -- 2. Evaluative Feedback -- 3. The Reinforcement Learning Problem -- II. Elementary Solution Methods -- 4. Dynamic Programming -- 5. Monte Carlo Methods -- 6. Temporal-Difference Learning -- III. A Unified View -- 7. Eligibility Traces -- 8. Generalization and Function Approximation -- 9. Planning and Learning -- 10. Dimensions of Reinforcement Learning -- 11. Case Studies -- References -- Summary of Notation -- Index.
Review: "In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. 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."--Jacket.
Tags from this library: No tags from this library for this title. Log in to add tags.
Star ratings
    Average rating: 0.0 (0 votes)
No physical items for this record

Contents -- Series Foreword -- Preface -- I. The Problem -- 1. Introduction -- 2. Evaluative Feedback -- 3. The Reinforcement Learning Problem -- II. Elementary Solution Methods -- 4. Dynamic Programming -- 5. Monte Carlo Methods -- 6. Temporal-Difference Learning -- III. A Unified View -- 7. Eligibility Traces -- 8. Generalization and Function Approximation -- 9. Planning and Learning -- 10. Dimensions of Reinforcement Learning -- 11. Case Studies -- References -- Summary of Notation -- Index.

Open Access Unrestricted online access star

"In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. 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."--Jacket.

Description based on print version record.

There are no comments on this title.

to post a comment.