Reinforcement Learning: An Introduction, 2nd Edition – Sutton & Barto (ISBN 9780262039246)

Reinforcement Learning: An Introduction (2nd Edition) by Richard S. Sutton and Andrew G. Barto is the definitive textbook on reinforcement learning, one of the most active and exciting areas in artificial intelligence. Published by MIT Press, this edition expands on the landmark first edition with new chapters, algorithms, and examples. Advance your AI expertise with Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto. This 552-page paperback, published in 2018, offers a definitive guide to reinforcement learning, enhanced for 2025 with deep learning and real-world applications.

Product Details

• Title: Reinforcement Learning: An Introduction
• Edition: 2nd (2018, MIT Press)
• Authors: Richard S. Sutton, Andrew G. Barto
• ISBN-13: 9780262039246
• Format: Hardcover
• Pages: 552
• Dimensions: 7 x 10 inches
• Price: $69.99 (Amazon: $105+)
• Availability: In Stock | Free shipping on orders over $50

At BooksGoat, we offer the official Sutton & Barto Reinforcement Learning textbook at a lower price than Amazon. Plus, take our AI basics quiz: score 75% or higher and receive an instant 5% discount.

Key Features of Reinforcement Learning (2nd Edition)

• Expanded Content: New material on policy gradient methods, deep reinforcement learning, and exploration strategies.
• Mathematical Foundation: Rigorous yet accessible treatment of Markov decision processes and algorithms.
• Algorithms and Code: Includes pseudocode and references for implementation in Python and other languages.
• Practical Applications: Robotics, game playing (e.g., AlphaGo), and decision-making systems.
• Authoritative Source: Written by two pioneers who shaped reinforcement learning research.

Table of Contents Highlights

1. Introduction to Reinforcement Learning
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
9. On-Policy Prediction and Control
10. Off-Policy Methods
11. Approximate Solution Methods
12. Policy Gradient Methods
13. Applications and Case Studies
14. Perspectives on Reinforcement Learning

Who Should Buy This Book?

• Computer science and AI students studying machine learning.
• Researchers and practitioners developing reinforcement learning systems.
• Engineers applying RL to robotics, games, and control systems.
• Anyone preparing for advanced coursework or research in artificial intelligence.

Why Choose BooksGoat?

• Lowest Price: $89.99 vs Amazon’s $105+
• Free Shipping: On U.S. orders over $50
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FAQs

Is this the official MIT Press edition?

Yes, this is the official 2nd Edition hardcover published in 2018.

What’s new in the 2nd Edition?

It includes expanded coverage of deep reinforcement learning, policy gradient methods, and recent research advances.

Do I need advanced math?

The book requires knowledge of probability, linear algebra, and basic calculus. It is designed for graduate-level study.

Is it suitable for self-study?

Yes, many students and practitioners use it as a self-study resource for reinforcement learning.

Final Call – Buy Now

Reinforcement Learning: An Introduction (2nd Edition) is the definitive guide to reinforcement learning, widely adopted in AI research and teaching. Order from BooksGoat today for $69.99 and stay ahead in the world of artificial intelligence. Free shipping and quiz discount available.