Copyright
Brief Table of Contents
Table of Contents
Preface
Acknowledgments
About This Book
About the Authors
About the Cover Illustration
1. Foundations
Chapter 1. What is reinforcement learning?
1.1. The “deep” in deep reinforcement learning
1.2. Reinforcement learning
1.3. Dynamic programming versus Monte Carlo
1.4. The reinforcement learning framework
1.5. What can I do with reinforcement learning?
1.6. Why deep reinforcement learning?
1.7. Our didactic tool: String diagrams
1.8. What’s next?
Summary
Chapter 2. Modeling reinforcement learning problems: Markov decision processes
2.1. String diagrams and our teaching methods
2.2. Solving the multi-arm bandit
2.2.1. Exploration and exploitation
2.2.2. Epsilon-greedy strategy
2.2.3. Softmax selection policy
2.3. Applying bandits to optimize ad placements