Table of contents : 1. Introduction 2. The Distribution of Returns 3. Learning the Return Distribution 4. Operators and Metrics 5. Distributional Dynamic Programming 6. Incremental Algorithms 7. Control 8. Statistical Functionals 9. Linear Function Approximation 10. Deep Reinforcement Learning 10.1. Learning with a Deep Neural Network 10.2. Distributional Reinforcement Learning with Deep Neural Networks 10.3. Implicit Parameterizations 10.4. Evaluation of Deep Reinforcement Learning Agents 10.5. How Predictions Shape State Representations 10.6. Technical Remarks 10.7. Bibliographical Remarks 10.8. Exercises 11. Two Applications and a Conclusion Notation References Index Series List