Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/Paulescu/hands-on-rl

Free course that takes you from zero to Reinforcement Learning PRO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½
https://github.com/Paulescu/hands-on-rl

deep-reinforcement-learning reinforcement-learning

Last synced: 3 months ago
JSON representation

Free course that takes you from zero to Reinforcement Learning PRO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½

Awesome Lists containing this project

README 

        


The Hands-on Reinforcement Learning course πŸš€ 


From zero to HERO πŸ¦ΈπŸ»β€πŸ¦ΈπŸ½


Out of intense complexities, intense simplicities emerge.


-- Winston Churchill





![](http://datamachines.xyz/wp-content/uploads/2021/11/PHOTO-2021-11-05-13-54-11.jpg)



[![Twitter Follow](https://img.shields.io/twitter/follow/paulabartabajo_?label=Follow&style=social)](https://twitter.com/paulabartabajo_)



## Contents



* [Welcome to the course](#welcome-to-the-course-)

* [Lectures](#lectures)

* [Wanna contribute?](#wanna-contribute)

* [Let's connect!](#lets-connect)



## Welcome to the course πŸ€—β€οΈ



Welcome to my step by step hands-on-course that will take you from basic reinforcement learning to cutting-edge deep RL.



We will start with a short intro of what RL is, what is it used for, and how does the landscape of current

RL algorithms look like.



Then, in each following chapter we will solve a different problem, with increasing difficulty:

- πŸ† easy

- πŸ†πŸ† medium

- πŸ†πŸ†πŸ†  hard



Ultimately, the most complex RL problems involve a mixture of reinforcement learning algorithms, optimizations and Deep Learning techniques.



You do not need to know deep learning (DL) to follow along this course.



I will give you enough context to get you familiar with DL philosophy and understand

how it becomes a crucial ingredient in modern reinforcement learning.



## Lectures



0. [Introduction to Reinforcement Learning](https://datamachines.xyz/2021/11/17/hands-on-reinforcement-learning-course-part-1/)

1. [Q-learning to drive a taxi πŸ†](01_taxi/README.md)

2. [SARSA to beat gravity πŸ†](02_mountain_car/README.md)

3. [Parametric Q learning to keep the balance πŸ’ƒ πŸ†](03_cart_pole/README.md)

4. [Policy gradients to land on the Moon πŸ†](04_lunar_lander/README.md)



## Wanna contribute?



There are 2 things you can do to contribute to this course:



1. Spread the word and share it on [Twitter](https://ctt.ac/Aa7dt), [LinkedIn](https://www.linkedin.com/shareArticle?mini=true&url=http%3A//datamachines.xyz/the-hands-on-reinforcement-learning-course-page/&title=The%20hands-on%20Reinforcement%20Learning%20course&summary=Wanna%20learn%20Reinforcement%20Learning?%20%F0%9F%A4%94%0A%40paulabartabajo%20has%20a%20course%20on%20%23reinforcementlearning,%20that%20takes%20you%20from%20zero%20to%20PRO%20%F0%9F%A6%B8%F0%9F%8F%BB%E2%80%8D%F0%9F%A6%B8%F0%9F%8F%BD.%0A%0A%F0%9F%91%89%F0%9F%8F%BD%20With%20lots%20of%20Python%0A%F0%9F%91%89%F0%9F%8F%BD%20Intuitions,%20tips%20%26%20tricks%20explained.%0A%F0%9F%91%89%F0%9F%8F%BD%20And%20free,%20by%20the%20way.%0A%0AReady%20to%20start?%20Click%20%F0%9F%91%87%F0%9F%8F%BD%F0%9F%91%87%F0%9F%8F%BE%F0%9F%91%87%F0%9F%8F%BF%0A%0A%23MachineLearning&source=)



2. Open a [pull request](https://github.com/Paulescu/hands-on-rl/pulls) to fix a bug or improve the code readability.



### Thanks ❀️

Special thanks to all the students who contributed with valuable feedback

and pull requests ❀



- [Neria Uzan](https://www.linkedin.com/in/neria-uzan-369803107/)

- [Anthony Lapadula](https://www.linkedin.com/in/anthony-lapadula-9343a5b/)

- [Petar Sekulić](https://www.linkedin.com/in/petar-sekulic-ml/)



## Let's connect!



πŸ‘‰πŸ½ Subscribe for **FREE** to the [Real-World ML newsletter](https://realworldml.net/subscribe/) 🧠



πŸ‘‰πŸ½ Follow me on [Twitter](https://twitter.com/paulabartabajo_) and [LinkedIn](https://www.linkedin.com/in/pau-labarta-bajo-4432074b/) πŸ’‘