(In Progress)

Sutton and Barto’s Reinforcement Learning: An Introduction is a seminal textbook of Reinforcement Learning due to its thorough explanations accompanied with abundant examples. The book contains numerous insightful figures. These are implementations of the figures.

## Table of Contents

- Introduction
- Multi-armed Bandits
- Finite Markov Decision Processes
- Dynamic Programming
- Monte Carlo Methods
- Temporal-Difference Learning
- $n$-step Bootstrapping
- Planning and Learning with Tabular Methods
- On-policy Prediction with Approximation
- On-policy Control with Approximation
- Off-policy Methods with Approximation
- Eligibility Traces
- Policy Gradient Methods