(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

  1. Introduction
  2. Multi-armed Bandits
  3. Finite Markov Decision Processes
  4. Dynamic Programming
  5. Monte Carlo Methods
  6. Temporal-Difference Learning
  7. $n$-step Bootstrapping
  8. Planning and Learning with Tabular Methods
  9. On-policy Prediction with Approximation
  10. On-policy Control with Approximation
  11. Off-policy Methods with Approximation
  12. Eligibility Traces
  13. Policy Gradient Methods