Table of Contents

Series Foreword

Preface

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

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 Monto 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

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 Continuing Tasks

6.8 Games, Afterstates, and Other Special Cases

6.9 Summary

6.10 Bibliographical and Historical Remarks

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 Forward and Backward Views

7.5 Sarsa()

7.6 Q()

7.7 Eligibility Traces for Actor-Client 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

8.4 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