Reinforcement Learning:
An Introduction

Richard S. Sutton
and Andrew G. Barto

MIT Press, Cambridge, MA, 1998
A Bradford Book

Endorsements Code Solutions Figures Errata Course Slides

If you would like to order a copy of the book, or if you are qualified instructor and would like to see an examination copy, please see the MIT Press home page for this book. Or you might be interested in the reviews at amazon.com. There is also a Japanese translation available.

The table of contents of the book is given below, with associated HTML. The HTML version has a number of presentation problems, and its text is slightly different from the real book, but it may be useful for some purposes.

• Preface

  Part I: The Problem

• 1 Introduction
  • 1.1 Reinforcement Learning
  • 1.2 Examples
  • 1.3 Elements of Reinforcement Learning
  • 1.4 An Extended Example: Tic-Tac-Toe
  • 1.5 Summary
  • 1.6 History of Reinforcement Learning
  • 1.7 Bibliographical Remarks

• 2 Evaluative Feedback
  • 2.1 An n-armed Bandit Problem
  • 2.2 Action-Value Methods
  • 2.3 Softmax Action Selection
  • 2.4 Evaluation versus Instruction
  • 2.5 Incremental Implementation
  • 2.6 Tracking a Nonstationary Problem
  • 2.7 Optimistic Initial Values
  • 2.8 Reinforcement Comparison
  • 2.9 Pursuit Methods
  • 2.10 Associative Search
  • 2.11 Conclusion
  • 2.12 Bibliographical and Historical Remarks

• 3 The Reinforcement Learning Problem
  • 3.1 The Agent-Environment Interface
  • 3.2 Goals and Rewards
  • 3.3 Returns
  • 3.4 A Unified Notation for Episodic and Continual Tasks
  • 3.5 The Markov Property
  • 3.6 Markov Decision Processes
  • 3.7 Value Functions
  • 3.8 Optimal Value Functions
  • 3.9 Optimality and Approximation
  • 3.10 Summary
  • 3.11 Bibliographical and Historical Remarks

  Part II: Elementary Methods

• 4 Dynamic Programming
  • 4.1 Policy Evaluation
  • 4.2 Policy Improvement
  • 4.3 Policy Iteration
  • 4.4 Value Iteration
  • 4.5 Asynchronous Dynamic Programming
  • 4.6 Generalized Policy Iteration
  • 4.7 Efficiency of Dynamic Programming
  • 4.8 Summary
  • 4.9 Historical and Bibliographical Remarks

• 5 Monte Carlo Methods
  • 5.1 Monte Carlo Policy Evaluation
  • 5.2 Monte Carlo Estimation of Action Values
  • 5.3 Monte Carlo Control
  • 5.4 On-Policy Monte Carlo Control
  • 5.5 Evaluating One Policy While Following Another
  • 5.6 Off-Policy Monte Carlo Control
  • 5.7 Incremental Implementation
  • 5.8 Summary
  • 5.9 Historical and Bibliographical Remarks

• 6 Temporal Difference Learning
  • 6.1 TD Prediction
  • 6.2 Advantages of TD Prediction Methods
  • 6.3 Optimality of TD(0)
  • 6.4 Sarsa: On-Policy TD Control
  • 6.5 Q-learning: Off-Policy TD Control
  • 6.6 Actor-Critic Methods (*)
  • 6.7 R-Learning for Undiscounted Continual Tasks (*)
  • 6.8 Games, After States, and other Special Cases
  • 6.9 Conclusions
  • 6.10 Historical and Bibliographical Remarks

  Part III: A Unified View

• 7 Eligibility Traces
  • 7.1 n-step TD Prediction
  • 7.2 The Forward View of TD()
  • 7.3 The Backward View of TD()
  • 7.4 Equivalence of the Forward and Backward Views
  • 7.5 Sarsa()
  • 7.6 Q()
  • 7.7 Eligibility Traces for Actor-Critic Methods (*)
  • 7.8 Replacing Traces
  • 7.9 Implementation Issues
  • 7.10 Variable (*)
  • 7.11 Conclusions
  • 7.12 Bibliographical and Historical Remarks

• 8 Generalization and Function Approximation
  • 8.1 Value Prediction with Function Approximation
  • 8.2 Gradient-Descent Methods
  • 8.3 Linear Methods
    • 8.3.1 Coarse Coding
    • 8.3.2 Tile Coding
    • 8.3.3 Radial Basis Functions
    • 8.3.4 Kanerva Coding
  • 8.4 Control with Function Approximation
  • 8.5 Off-Policy Bootstrapping
  • 8.6 Should We Bootstrap?
  • 8.7 Summary
  • 8.8 Bibliographical and Historical Remarks

• 9 Planning and Learning
  • 9.1 Models and Planning
  • 9.2 Integrating Planning, Acting, and Learning
  • 9.3 When the Model is Wrong
  • 9.4 Prioritized Sweeping
  • 9.5 Full vs. Sample Backups
  • 9.6 Trajectory Sampling
  • 9.7 Heuristic Search
  • 9.8 Summary
  • 9.9 Historical and Bibliographical Remarks

• 10 Dimensions
  • 10.1 The Unified View
  • 10.2 Other Frontier Dimensions

• 11 Case Studies
  • 11.1 TD-Gammon
  • 11.2 Samuel's Checkers Player
  • 11.3 The Acrobot
  • 11.4 Elevator Dispatching
  • 11.5 Dynamic Channel Allocation
  • 11.6 Job-Shop Scheduling

• References
• Summary of Notation