Reinforcement Learning: An Introduction, 2nd Edition
- Length: 322 pages
- Edition: 2
- Language: English
- Publisher: A Bradford Book
- Publication Date: 1998-03-01
- ISBN-10: 0262193981
- ISBN-13: 9780262193986
- Sales Rank: #125979 (See Top 100 Books)
Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications.
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 when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field’s intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.
The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.
Cover Title Page Copyright Dedication Contents Foreword Preface 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. Conclusions 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. Unified Notation for Episodic and Continuing 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 II. Elementary Solution 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. Bibliographical and Historical 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. Bibliographical and Historical Remarks 6. Temporal-Difference Learning 133 6.1. TD Prediction 133 6.2. Advantages of TD Prediction Methods 138 6.3. Optimality of TD(0) 141 6.4. Sarsa: On-Policy TD Control 145 6.5. Q-Learning: Off-Policy TD Control 148 * 6.6. Actor–Critic Methods 151 * 6.7. R-Learning for Undiscounted Continuing Tasks 153 6.8. Games, Afterstates, and Other Special Cases 156 6.9. Summary 157 6.10 Bibliographical and Historical Remarks III. A Unified View 7. Eligibility Traces 7.1. n-Step TD Prediction 7.2. The Forward View of TD(λ) 7.3. The Backward View of TD(λ) 7.4. Equivalence of Forward and Backward Views 7.5. Sarsa(λ) 7.6. Q(λ) * 7.7. Eligibility Traces for Actor–Critic Methods 7.8. Replacing Traces 7.9. Implementation Issues * 7.10. Variable λ 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 8.4. 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. Bibliographical and Historical Remarks 10. Dimensions of Reinforcement Learning 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 Index
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