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Reinforcement learning : an introduction

Author: Richard S Sutton; Andrew G Barto
Publisher: Cambridge, Massachusetts : The MIT Press, [2018]
Series: Adaptive computation and machine learning.
Edition/Format:   eBook : Document : English : Second editionView all editions and formats
Summary:
"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 while interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the field's key ideas and algorithms."--
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Sutton, Richard S.
Reinforcement learning.
Cambridge, Massachusetts : The MIT Press, [2018]
(DLC) 2018023826
(OCoLC)1043175824
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Richard S Sutton; Andrew G Barto
ISBN: 9780262352703 0262352702
OCLC Number: 1091191532
Description: 1 online resource (xxii, 526 pages)
Contents: 1. Introduction --
I. Tabular Solution Methods: 2. Multi-armed Bandits --
3. Finite Markov Decision processes --
4. Dynamic programming --
5. Monte Carlo methods --
6. Temporal-difference learning --
7. n-step Bootstrapping --
8. Planning and learning with tabular methods--
I. Approximate Solution Methods: 9. On-policy Prediction with Approximation--
10. On-policy Control with Approximation--
11. O↵-policy Methods with Approximation --
12. Eligibility Traces--
13. Policy Gradient Methods--
III. Looking Deeper: 14. Psychology --
15. Neuroscience --
16. Applications and Case Studies --
17. Frontiers
Series Title: Adaptive computation and machine learning.
Responsibility: Richard S. Sutton and Andrew G. Barto.

Abstract:

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.  Read more...

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