Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto
This repository contains a python implementation of the concepts described in the book Reinforcement Learning: An Introduction, by Sutton and Barto.
For each chapter you will find a .py file that contains the main implementation, and a .ipynb used to quickly visualise figures on github.com.
The repository is still WIP. I will try to move linearly ahead with the book, you can check below for a roadmap of the immadiate actions.
Please, feel free to raise issues to ask questions or flag flaws and mistakes in the implementation.
Should you find this useful for you, I would be grateful if you'd star:star: it :)
Available problems
Chapter 1: Introduction
Chapter 2: Multi-armed Bandits
- Scheduled
Chapter 3: Finite Markov Decision Processes
- Scheduled
Chapter 4: Dynamic Programming (figures)
- GridWorld: Dynamic Programming Prediction with Iterative Policy Evaluation
- Jack's car rental: Dynamic Programming Control with Policy Iteration
- Gambler's Problem: Dynamic Programming Control with Value Iteration
Chapter 5: Monte Carlo Methods (figures)
- Blackjack: Monte Carlo Prediction with Policy Evaluation
- Blackjack: Single state-value estimation
- [WIP] [Blackjack: Monte Carlo Control with Monte Carlo ES]()
- [WIP] [On-Policy first-visit Monte Carlo Control]()
- [WIP] [Off-Policy Monte Carlo Prediction via Importance Sampling]()
- [WIP] [Off-Policy Monte Carlo Prediction with Policy Evaluation]()
- [WIP] [Off-Policy Monte Carlo Control]()
References
[1] R. S. Sutton, A. G. Barto, et al. Reinforcement Learning: an Introduction. MIT press, Cambridge, 2018.
[2] Original Code, 2nd Edition. http://incompleteideas.net/book/code/code2nd.html