I have experimented a lot with the "official" Common Lisp and Python examples for the Sutton/Barto RL book, and I will enjoy your implementations also!

For reference, original examples in Lisp and Python: http://incompleteideas.net/book/code/code2nd.html

A bunch of implementations with all kinds of use cases (e.g., using OpenAI RL Gym, etc.):

Here are some resources with code examples and implementations related to the Sutton and Barto "Reinforcement Learning: An Introduction" book:

Code for Sutton & Barto Book: Reinforcement Learning: An Introduction: The official website for the book provides links to various software and re-implementations in different languages, including Python, Julia, and Lisp. This is a great starting point to find code directly associated with the book's examples and exercises.

Link: http://incompleteideas.net/book/code/code2nd.html jovsa/rl-examples-sutton-and-barto-book on GitHub: This repository offers Python implementations of examples from the book, organized by chapter. It includes code for figures and examples from various chapters, covering topics like Gridworld, Blackjack, and the Mountain Car task.

Link: https://github.com/jovsa/rl-examples-sutton-and-barto-book kamenbliznashki/sutton_barto on GitHub: This repository provides Python implementations of RL algorithms for the examples and figures in the Sutton and Barto book. It covers a wide range of topics from multi-armed bandits to policy gradient methods.

Link: https://github.com/kamenbliznashki/sutton_barto boldyshev/sutton on GitHub: This repository contains Python implementations of example experiments (figures) and programming exercises from the second edition of the book. Chapters are added as the author studies the book, making it a potentially growing resource.

Link: https://github.com/boldyshev/sutton AntonioSerrano/Implementation-of-RL-algorithms-from-Sutton-and-Barto-2018 on GitHub: This repository offers implementations in Python using OpenAI Gym and Tensorflow, covering exercises and solutions to complement the book and David Silver's RL course. It includes various algorithms like Dynamic Programming, Monte Carlo, Temporal Difference, and Policy Gradient methods.

Link: https://github.com/AntonioSerrano/Implementation-of-RL-algor...

Professors White & White (a husband and wife team) have a very good set of courses on RL on Coursera:

https://www.coursera.org/specializations/reinforcement-learn...

https://www.cics.umass.edu/

https://www.nsf.gov/news/ai-pioneers-andrew-barto-richard-su...