Tags: Reinforcement Learning, Machine Learning, Sutton, Barto, AI, Deep Learning, Neural Networks, MIT Press, Artificial Intelligence, Data Science, Algorithms
Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto, and Francis Bach (ISBN 9780262039246) - Hardcover
- Type: Hardcover Book
- Language : English
Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto, and Francis Bach (ISBN 9780262039246)
Reinforcement Learning: An Introduction by Richard S. Sutton, Andrew G. Barto, and Francis Bach is the definitive text on the theory and algorithms behind reinforcement learning — one of the core branches of modern artificial intelligence. Published by MIT Press, this second edition (2018) expands the classic framework that has trained generations of AI researchers and engineers.
Product Details
- Title: Reinforcement Learning: An Introduction
- Authors: Richard S. Sutton, Andrew G. Barto, Francis Bach
- Publisher: MIT Press
- Publication Date: 23 November 2018
- ISBN-13: 9780262039246
- Format: Hardcover / Illustrated
- Pages: 552 + diagrams and algorithmic examples
- Price: £36.99
- Minimum Order: 3 Copies
- Availability: In Stock | Free UK & EU Shipping
Building on the original 1998 edition, this updated version introduces deep reinforcement learning concepts, function approximation, and modern algorithms like policy gradients and actor-critic models. Sutton and Barto combine clarity of explanation with mathematical depth, making it an indispensable reference for AI professionals, computer scientists, and advanced learners.
Key Features
- Comprehensive Theory: Explains the core principles of reinforcement learning — reward signals, policy optimisation, and value functions.
- Modern Algorithms: Includes Q-learning, Monte Carlo methods, temporal difference learning, and deep RL architectures.
- Practical Foundations: Bridges theory with implementation, featuring pseudo-code and intuitive diagrams.
- New Material: Updated coverage on off-policy methods, eligibility traces, and generalised policy iteration.
- Globally Acclaimed Textbook: Used in leading universities and AI labs including DeepMind and OpenAI.
Table of Contents Highlights
- Introduction to Reinforcement Learning
- Multi-Armed Bandits and Decision Processes
- Dynamic Programming and Value Iteration
- Monte Carlo Methods and Temporal-Difference Learning
- Function Approximation and Generalisation
- Policy Gradient and Actor-Critic Algorithms
- Planning, Exploration, and Exploitation
- Deep Reinforcement Learning and Future Directions
Who Should Use This Book?
- AI researchers and machine learning professionals
- Data scientists and software engineers
- University students in computer science, robotics, and applied mathematics
- Practitioners exploring RL applications in finance, robotics, and automation
Why Buy from BooksGoat UK?
- Best Price: £36.99 (Min 3 copies) vs RRP £46.99
- Free UK & EU Shipping
- Authentic MIT Press Edition verified ISBN 9780262039246
- Eco-Friendly Packaging and fast UK dispatch
FAQs
- Is this the updated 2nd Edition?
- Yes — the 2018 MIT Press second edition includes expanded chapters and contributions by Francis Bach.
- Does it include deep reinforcement learning?
- Yes — this edition introduces deep RL, actor-critic, and modern gradient-based optimisation methods.
- Is this suitable for beginners?
- It assumes some familiarity with linear algebra and probability but explains all RL concepts from first principles.
Final Call – Order Now
Reinforcement Learning: An Introduction by Sutton, Barto, and Bach remains the most influential AI textbook on learning through interaction and feedback. Order now from BooksGoat.co.uk for just £36.99 (min 3 copies). Free UK & EU shipping included.
