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A python implementation of the concepts in the book "Reinforcement Learning: An Introduction" by R.S. Sutton and A. G. Barto.

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Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public. License: MIT

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⭐ it :)

Available problems

Chapter 1: Introduction

Chapter 2: Multi-armed Bandits

  • Scheduled

Chapter 3: Finite Markov Decision Processes

  • Scheduled

Chapter 4: Dynamic Programming (figures)

Chapter 5: Monte Carlo Methods (figures)

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