Contents

• I. The Problem
  • 1. Introduction
  • 2. Evaluative Feedback
  • 3. The Reinforcement Learning Problem
  
  
• II. Elementary Solution Methods
  • 4. Dynamic Programming
  • 5. Monte Carlo Methods
  • 6. Temporal-Difference Learning
  
  
• III. A Unified View
  • 7. Eligibility Traces
  • 8. Generalization and Function Approximation
  • 9. Planning and Learning
  • 10. Dimensions of Reinforcement Learning
  • 11. Case Studies
  
  
• Bibliography