Description
Reinforcement Learning: An Introduction, Second Edition (Sutton & Barto)
Ask anyone working in reinforcement learning where to start and this is the answer you get. Richard Sutton and Andrew Barto wrote the field’s standard text, and the second edition — significantly expanded and updated from the 1998 original — remains the reference that university courses, research groups and self-taught practitioners build on.
What sets the book apart is its refusal to hide behind formalism. The core ideas — value, policy, exploration versus exploitation, temporal-difference learning — are developed in plain language first, with the mathematics available for those who want it rather than imposed on those who do not.
How the Book Is Organised
- Part I: Tabular solution methods — multi-armed bandits, Markov decision processes, dynamic programming, Monte Carlo methods, temporal-difference learning, n-step bootstrapping and planning. Everything here admits exact solutions, which makes the ideas easy to see clearly.
- Part II: Approximate solution methods — on-policy and off-policy prediction and control with function approximation, eligibility traces, and policy gradient methods, extending the tabular ideas to problems too large to enumerate.
- Part III: Looking deeper — psychology and neuroscience connections, and applications and case studies that show the methods at work.
New in the Second Edition
- Substantially expanded coverage, with new topics added and existing ones brought up to date.
- Continued focus on core online learning algorithms rather than a survey of everything.
- Mathematical material set off in shaded boxes so the narrative stays accessible.
- Updated case studies and a fuller treatment of the links to psychology and neuroscience.
Who Should Read It
- Students taking a first course in reinforcement learning or advanced machine learning.
- Machine learning engineers and researchers who want the foundations behind modern deep RL, not just the recipes.
- Robotics, control and operations research practitioners working on sequential decision problems.
- Anyone moving into RL from a general AI or data science background.
Book Details
- Title: Reinforcement Learning: An Introduction
- Authors: Richard S. Sutton and Andrew G. Barto
- Edition: Second Edition
- Series: Adaptive Computation and Machine Learning
- Publisher: Bradford Books / The MIT Press
- Publication date: November 13, 2018
- Format: Hardcover, 552 pages
- Language: English
- ISBN-13: 978-0262039246
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