Reinforcement Learning (RL) is a complex topic comprising of terminology and concepts that all seem to blend together. In this section, we uncover the terminology and basics of RL for the novice or more advanced user.
This section contains the following chapters:
- Chapter 1, Understanding Rewards-Based Learning
- Chapter 2, Monte Carlo Methods
- Chapter 3, Dynamic Programming and the Bellman Equation
- Chapter 4, Temporal Difference Learning
- Chapter 5, Exploring SARSA