The Reinforcement Learning Playground
Contents
The Reinforcement Learning Playground¶
Welcome to the Reinforcement Learning Playground! We are happy that you have decided to give our book a try.
The RL-playground is an open-source and interactive book project that has been initiated, hoping to inspire people to get into the exciting field of Reinforcement Learning. The book itself has been created with the help of many maintainers and contributors.
The book will cover the mathematical theories of multiple topics from the field. However, whenever possible, these will be accompanied by code implementations and examples. This aims to make learning more interactive, practical and fun!
The target audience of this book is anyone who would want to learn more about reinforcement learning. The content is expected to cover fundamental theories to state-of-the-art research.
Tip
While you do not have to know anything about Reinforcement Learning prior to starting with the book, one is expected to
have some previous experience with Calculus, Linear Algebra, Probability Theory, and Statistics.
Furthermore, for software implementations, Python will be used. Within Python, we make use of well-known packages to
make our lives easier, such as Pytorch, OpenAI Gym, Numpy. If you are interested in the practical side
of the book, please make sure you have a basic understanding of these software packages.
Book content¶
The book is currently a work in progress, but eventually, the following topics should be discussed (may change over time):
Topic |
Completed |
|---|---|
Fundamentals of Reinforcement Learning |
PDF only (conversion in progress) |
Deep Reinforcement Learning |
In progress |
Policy Gradient Algorithms |
|
Model-based Reinforcement Learning |
|
Hierarchical Reinforcement Learning |
|
Distributional Reinforcement Learning |
|
Evolutionary Reinforcement Learning |
|
Unsupervised Reinforcement Learning |
|
Meta Reinforcement Learning |
|
Multiagent Reinforcement Learning |
|
Inverse Reinforcement Learning |
One chapter, PDF only (conversion in progress) |
Citations¶
Please cite this book (APA) as
Quaedvlieg, L. C. P. M. (2022). The reinforcement learning playground. https://lars-quaedvlieg.github.io/RL-Playground
@book{quaedvliegRLP2022,
title={The reinforcement learning playground},
author={Quaedvlieg, L.C.P.M.},
url={https://lars-quaedvlieg.github.io/RL-Playground},
year={2022}
}