DistilRL: A Condensed Introduction

I. Introduction

I. Introduction

• Chapter 0. Prelude
• Chapter 1. Introduction to RL

II. Tabular Solution Methods

II.1. Fundamentals of Reinforcement Learning

• Chapter 2. Multi-armed Bandit
• Chapter 3. Finite Markov Decision Processes
• Chapter 4. Dynamic Programming

II.2. Sample-based Learning Methods

• Chapter 5. Monte Carlo Methods
• Chapter 6. Temporal-Difference Learning
• Chapter 7. Planning and Learning with Tabular Methods

III. Approximate Solution Methods

III.1. Value Function Approximation

• Chapter 8. On-policy Prediction with Approximation
• Chapter 9. On-policy Control with Approximation

III.2. Policy Approximation

• Chapter 10. Policy Gradient Methods
• Chapter 11. Modern Policy Gradient Methods