• RRP: £58.00
  • You Save: £20.60 (36%)
FREE Delivery in the UK.
In stock.
Dispatched from and sold by Amazon. Gift-wrap available.
Reinforcement Learning: A... has been added to your Basket
+ £2.80 delivery
Used: Good | Details
Condition: Used: Good
Comment: Ships from the UK. Former Library books. Shows some signs of wear, and may have some markings on the inside. 100% Money Back Guarantee.
Have one to sell?
Flip to back Flip to front
Listen Playing... Paused   You're listening to a sample of the Audible audio edition.
Learn more
See all 2 images

Follow the Author

Something went wrong. Please try your request again later.


Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series) Hardcover – 8 May 1998

5.0 out of 5 stars 2 customer reviews

See all 4 formats and editions Hide other formats and editions
Amazon Price
New from Used from
Hardcover
£37.40
£32.64 £32.64
Promotion Message Save £5.00 on orders £25.00 or more 1 promotion

Note: This item is eligible for click and collect. Details
Pick up your parcel at a time and place that suits you.
  • Choose from over 13,000 locations across the UK
  • Prime members get unlimited deliveries at no additional cost
How to order to an Amazon Pickup Location?
  1. Find your preferred location and add it to your address book
  2. Dispatch to this address when you check out
Learn more

Great Discounts
Shop the Books Outlet. Discover some great deals on top titles. Shop now
click to open popover

Special offers and product promotions

  • Get £5 off qualifying orders of £25 or more. Today only. Ts&Cs apply. Enter code BIGTHANKS at checkout. Here's how (terms and conditions apply)
  • Buy this product and stream 90 days of Amazon Music Unlimited for free. E-mail after purchase. Conditions apply. Learn more

Frequently bought together

  • Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning series)
  • +
  • Deep Learning (Adaptive Computation and Machine Learning Series)
  • +
  • Pattern Recognition and Machine Learning (Information Science and Statistics)
Total price: £134.55
Buy the selected items together

Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.

  • Apple
    Apple
  • Android
    Android
  • Windows Phone
    Windows Phone

To get the free app, enter your mobile phone number.

kcpAppSendButton


Product details

  • Hardcover: 344 pages
  • Publisher: MIT Press; second edition edition (8 May 1998)
  • Language: English
  • ISBN-10: 9780262193986
  • ISBN-13: 978-0262193986
  • ASIN: 0262193981
  • Product Dimensions: 17.8 x 2.7 x 22.9 cm
  • Average Customer Review: 5.0 out of 5 stars 2 customer reviews
  • Amazon Bestsellers Rank: 108,422 in Books (See Top 100 in Books)
  • Would you like to tell us about a lower price?
    If you are a seller for this product, would you like to suggest updates through seller support?

  • See Complete Table of Contents

Product description

Synopsis

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. This text aims to provide a clear and simple account of the key ideas and algorithms of reinforcement learning. The 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 one defines the reinforcement learning problems in terms of Markov decision problems. Part two provides basic solution methods - dynamic programming, Monte Carlo simulation and temporal-difference learning - and part three 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.

From the Author

A unified approach to AI, machine learning, and control

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 this book, we provide an explanation of the key ideas and algorithms of reinforcement learning. The 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. This book is meant to be an introductory treatment of reinforcement learning, emphasizing foundations and ideas rather than the latest developments and mathematical proofs. We divide the ideas underlying the field into a half dozen primary dimensions, consider each in detail, and then combine them to form a much larger space of possible methods including all the most popular ones from Q-learning to value iteration and heuristic search. In this way we have tried to make the book interesting to both newcomers and experts alike. We have tried to make the work accessible to the broadest possible audiences in artificial intelligence, control engineering, operations research, psychology, and neuroscience. If you are a teacher, we urge you to consider creating or altering a course to use the book. We have found that the book works very well as the text for a course on reinforcement learning at the graduate or advanced undergraduate level. The eleven chapters can be covered one per week. Exercises are provided in each chapter to help the students think on their own about the material. Answers to the exercises are available to instructors, for now from me, and probably later from MIT Press in an instructor's manual. Programming projects are also suggested throughout the book. Of course, the book can also be used to help teach reinforcement learning as it is most commonly done now, that is, as part of a broader course on machine learning, artificial intelligence, neural networks, or advanced control. I have taught all the material in the book in as little as four weeks, and of course subsets can be covered in less time. Table of contents: Part I: The Problem 1 Introduction 2 Evaluative Feedback 3 The Reinforcement Learning Problem Part II: Elementary Methods 4 Dynamic Programming 5 Monte Carlo Methods 6 Temporal Difference Learning Part III: A Unified View 7 Eligibility Traces 8 Generalization and Function Approximation 9 Planning and Learning 10 Dimensions of Rreinforcement Learning 11 Case Studies For further information, see http://envy.cs.umass.edu/~rich/book/the-book.html.

See all Product description

2 customer reviews

5.0 out of 5 stars

Review this product

Share your thoughts with other customers

30 June 2011
Format: HardcoverVerified Purchase
8 people found this helpful
Comment Report abuse
6 May 2014
Format: HardcoverVerified Purchase
4 people found this helpful
Comment Report abuse

Most helpful customer reviews on Amazon.com

Amazon.com: 4.3 out of 5 stars 28 reviews
Bin Wang
5.0 out of 5 starsThis book has all the "whats", all the "whys" and all the "hows"!
3 February 2018 - Published on Amazon.com
Format: HardcoverVerified Purchase
12 people found this helpful.
Amazon Customer
5.0 out of 5 starsGood book. Good store.
13 July 2018 - Published on Amazon.com
Format: HardcoverVerified Purchase
One person found this helpful.
Dr. Lee D. Carlson
5.0 out of 5 starsAn excellent introduction
5 November 2004 - Published on Amazon.com
Format: HardcoverVerified Purchase
24 people found this helpful.
Ian Conway
5.0 out of 5 starsAn excellent read for anyone looking to learn about Reinforcement Learning
30 January 2018 - Published on Amazon.com
Format: HardcoverVerified Purchase
john l
1.0 out of 5 stars2nd edition available
21 November 2018 - Published on Amazon.com
Format: HardcoverVerified Purchase
Pages with related products. See and discover other items: deep learning